Journal Pre-proof
Towards intelligent industrial systems: A comprehensive survey of sensor fusion
techniques in IIoT
Deepak sharma, Anuj kumar, Nitin Tyagi, Sunil S. Chavan, Syam Machinathu
Parambil Gangadharan
PII:
S2665-9174(23)00280-5
DOI:
https://doi.org/10.1016/j.measen.2023.100944
Reference:
MEASEN 100944
To appear in:
Measurement: Sensors
Received Date: 29 December 2022
Revised Date:
3 October 2023
Accepted Date: 12 November 2023
Please cite this article as: D. sharma, A. kumar, N. Tyagi, S.S. Chavan, S.M.P. Gangadharan,
Towards intelligent industrial systems: A comprehensive survey of sensor fusion techniques in IIoT,
Measurement: Sensors (2024), doi: https://doi.org/10.1016/j.measen.2023.100944.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition
of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of
record. This version will undergo additional copyediting, typesetting and review before it is published
in its final form, but we are providing this version to give early visibility of the article. Please note that,
during the production process, errors may be discovered which could affect the content, and all legal
disclaimers that apply to the journal pertain.
© 2023 Published by Elsevier Ltd.
Towards Intelligent Industrial Systems: A Comprehensive Survey of Sensor
Fusion Techniques in IIoT
Deepak sharmaa , Anuj kumarb , Nitin Tyagic , Sunil S. Chavand , Syam Machinathu Parambil
Gangadharane
a Department of computer science, Aryabhatta college, University of Delhi, India
University school of Information, Communication and Technology, Guru Gobind Singh Indraprasth University, Dwarka,
India
c Department of Computer science and Engineering, GL Bajaj Institute of Technology and Management,Greater Noida, India
d Smt.Indira Gandhi College of engineering.Navi Mumbai. Affiliation: University of Mumbai, India
e Sr Data Engineer, The Home Depot, India
ro
of
b
Abstract
-p
Industrial Internet of Things (IIoT) is systems aim to facilitate human monitoring and the direction
of efficient production of goods in industrial settings by linking a wide variety of intelligent devices such
re
as sensors, actuators, and controllers. This is achieved by utilizing Internet of Things (IoT) to diagnose a
lP
problem with a specific IIoT part is to employ a basic diagnostic technique that’s based on models and data.
Physical models, signal patterns, and machine-learning strategies must be adequately built to account for
na
system challenges. Another factor that could lead to an exponential rise in complexity is the ever-increasing
interconnections between different electronic hardware. The knowledge-based defect diagnosis methods
ur
boost interoperability in the operation. Users don’t need to be experts in the field to benefit from the
system’s high-level thinking and response to their queries. So, in advanced IIoT systems, a knowledge-based
Jo
fault diagnostic approach is favored over traditional model-based and data-driven diagnosis methods. The
goal of this study is to evaluate recent improvements in the design of knowledge-based defect detection in
the context of IIoT systems, deductive and inductive reasoning, and many other forms of logical reasoning.
IIoT-based systems have revolutionized industrial settings by connecting intelligent devices such as sensors,
actuators, and controllers to enable efficient production and human monitoring. In this survey paper, we
explore machine learning-based sensor fusion techniques within the realm of Industrial Internet of Things
(IIoT), addressing critical challenges in fault detection and diagnosis.
Keywords: Sensor fusion; Machine learning; Fault tolerance; Fault prediction; Neural network
Email addresses: deepakdixit151@gmail.com ( Deepak sharma), sonanuj16@gmail.com (Anuj kumar),
Nitin.tyagi@glbitm.ac.in ( Nitin Tyagi),
chavanss @hotmail.com(SunilS.Chavan), syamganga@gmail.com(SyamM achinathuP arambilGangadharan)
Preprint submitted to Elsevier
December 6, 2023
Acronyms
Full Form
WSN
Wireless Sensor Network
IoT
Internet of Things
IIoT
Industrial Internet of Things
RTD
Resistance Temperature Detector
ANN
Artificial Neural Network
BPNN
Back Propagation Neural Network
DWT
Discrete Wavelet Transformation
EM
Expectation Maximization
EWMA
Exponentially Weighted Moving Average
FFT
Fast Fourier Transform
FPCA
Functional Principal Component Analysis
GMM
Gaussian Mixture Model
GRBM
Gaussian–Bernoulli Restricted Boltzmann Machine
GRNN
General Regression Neural Network
HMM
Hidden Markov Model
KNN
k-Nearest Neighbors
KDE
Kernel Density Estimator
LAD
Logical Analysis of Data
LOF
Local Outlier Factor
RBM
-p
re
lP
na
ur
Jo
PCA
ro
of
Acronym
Principal Component Analysis
Restricted Boltzmann Machine
RNN
Recurrent Neural Network
SARMA
Seasonal Autoregressive Moving Average
SBM
Similarity Based Modeling
SOM-MQE
Self-Organizing Map Minimize Quantization Error
SVM
Support Vector Machine
VCM
Vibration-based Condition Monitoring
1. Introduction
The goal of the IIoT is to allow humans to effectively monitor and direct the production of commodities
in industrial settings by connecting a wide variety of intelligent devices, such as sensors, actuators, and
controllers, to one another. The Internet of Things is used to achieve this goal such as to locate and isolate
2
a problem in certain IIoT parts, it may be sufficient to employ model-based and data-driven diagnostic tools
[1]. However, adequately developed physical models, signal patterns, and machine-learning methodologies
are necessary to explain system issues. Moreover, the ever-increasing number of interconnections among
devices has the potential to produce an exponential rise in complexity. These are used by knowledgebased defect diagnosis methods to boost interoperability [2]. As a result, the system can provide high-level
reasoning and query responses to individuals who aren’t specialists in the field. Consequently, advanced
IIoT techniques favor a knowledge-based fault diagnostic methodology over traditional, model-based, and
data-driven troubleshooting approaches [3, 4]. It’s because knowledge-based fault diagnostic methodology
ro
of
is more precise. This research aims to examine recent developments in the design of knowledge bases for
knowledge-based defect detection within the framework of IIoT systems, using ontologies, deductive and
inductive reasoning, and a wide variety of other forms of logical reasoning. Furthermore, methods for defect
identification in IIoT systems based on knowledge are explored; ways that make substantial use of inductive
-p
reasoning are given special attention.
re
The next generation of fault detection will require distributed implementations across the system, keeping
with the current trend toward decentralizing large systems. Since this is the case, therefore our study by
lP
pointing out some intriguing open questions concerning knowledge-based failure detection for distributed
IIoT systems.
na
• Predictive maintenance: Sensors can gather data on the performance and condition of equipment,
allowing for the identification of potential issues before they occur. This can help to reduce downtime
ur
and improve overall efficiency.
Jo
• Supply chain optimization: The IIoT helps lots to keep the track of goods and materials movement,
allowing for more efficient and timely delivery.
• Energy management: Sensors can be used to monitor and control energy use in industrial settings,
helping to reduce waste and improve efficiency.
• Quality control: Sensors can be used to gather data on the quality of products being produced, allowing
for the identification and correction of issues in real-time.
1.1. Differentiating Factors
The field of Industrial Internet of Things (IIoT) has garnered significant attention, resulting in the
availability of several survey papers in the literature. There are several research available which provided the
classification for the applicability of advance sensors and its applications [5, 6, 7, 8]. While acknowledging
the existence of related surveys, it is essential to highlight the unique aspects and additional information that
readers can gain from our survey paper titled ”A survey on machine learning-based sensor fusion techniques
in Industrial Internet of Things.”
3
1. Focus on Machine Learning-based Sensor Fusion Techniques: Unlike broader surveys that cover various aspects of IIoT, our survey paper specifically focuses on machine learning-based sensor fusion
techniques. The study delve deep into the design and integration of machine learning algorithms for
sensor data fusion in IIoT systems. This specialized focus allows us to provide in-depth insights into
the advancements, challenges, and potential applications of these techniques.
2. Comprehensive Review of Sensor Fusion Methods: Our survey paper provides a comprehensive review
of various machine learning-based sensor fusion methods, including probabilistic modeling, deep learning approaches, ensemble methods, and Bayesian inference, among others. The study highlight the
ro
of
strengths, limitations, and practical considerations of each method, enabling readers to gain a holistic
understanding of the available techniques and their suitability for different IIoT applications.
3. Emphasis on Industrial Applications and Real-world Implementations: While other surveys may discuss sensor fusion techniques in a generic context, our survey paper places a strong emphasis on their
-p
application in industrial settings. The study explore the challenges and requirements specific to IIoT
re
systems, such as robustness, reliability, scalability, and real-time processing. By focusing on industrial
applications, The study provide readers with practical insights and considerations for implementing
lP
sensor fusion techniques in real-world scenarios.
4. Additional Information: In addition to the differentiating factors mentioned above, readers can gain
na
the following additional information from our survey paper:
• Comparative Analysis: The study conduct a detailed comparative analysis of the performance,
ur
advantages, and limitations of different machine learning-based sensor fusion techniques. This
Jo
allows readers to make informed decisions and select appropriate fusion methods based on their
specific requirements and constraints.
• Case Studies and Use Cases: The study present case studies and use cases that showcase the
successful implementation of machine learning-based sensor fusion in various industrial domains.
These real-world examples provide readers with practical insights into the benefits and challenges
associated with deploying sensor fusion techniques in IIoT systems.
• Future Research Directions: Our survey paper identifies emerging research directions and open
challenges in the field of machine learning-based sensor fusion in IIoT. By discussing these future
research directions, the study inspire researchers to explore novel approaches, address existing
limitations, and contribute to the advancement of the field.
This review paper presents a comprehensive analysis of recent advancements in knowledge-based defect
detection for IIoT systems. It uniquely emphasizes the integration of different learning techniques, properties
and the relations between them, deductive and inductive reasoning, and logical methodologies to enhance
fault diagnosis capabilities. By showcasing the integration of diverse strategies, this paper sheds light on
4
ro
of
-p
Figure 1: Different Components of Industry 4.0 [2].
a novel approach to tackle complexities in IIoT systems. Our investigation reveals that a learning-based
re
fault diagnostic approach outperforms traditional model-based and data-driven methods in advanced IIoT
systems. By leveraging connection among data and logical reasoning, the system’s high-level thinking and
lP
responsiveness to queries has been enhanced. The practical significance of existing learning approach is
evident in its ability to provide accurate and rapid defect identification, leading to reduced downtime and
na
optimized production processes. The application of machine learning-based sensor fusion techniques in IIoT
holds immense practical value. Industries benefit from improved fault detection, predictive maintenance,
ur
and enhanced operational efficiency. The role in industrial applications is pivotal as it facilitates real-time
Jo
monitoring, process optimization, and adaptive decision-making, resulting in substantial cost savings and
increased competitiveness.
This paper focuses on the machine-learning techniques that are used for sensor fusion in IIoTs as structure
is shown in Fig. 2. Sensor fusion and its underlying taxonomy are laid out in (Section II)—existing literature
on sensor fusion and its categorization (Section III). Describe the most important features of the sensor fusion
approach used in machine learning for IIoT, provide a comprehensive survey based on parameters, and then
draw a conclusion (Section IV).
1.2. Existing literature and its classification for sensor fusion solution or approach in IIoT
The Table.1 presents a comparison of existing surveys in the field of Industrial Internet of Things (IIoT)
and Industry 4.0. These surveys have explored various aspects of IIoT, including data fusion, predictive
analysis, manufacturing processes, machine learning, big data analytics, and additive manufacturing. While
these surveys have provided valuable insights into different facets of IIoT, our forthcoming survey aims to
build upon this foundational knowledge. With a focus on synthesizing the latest advancements and emerging
trends, our survey is poised to offer a comprehensive and up-to-date perspective on the state of the field.
5
ro
of
-p
re
lP
na
ur
Jo
Figure 2: Diagram illustrating the hierarchical structure of the paper, including sections and subsections, providing an
overview of the paper’s organization.
6
Table 1: Comparative Analysis of Existing Surveys in the Field of Industrial Internet of Things (IIoT) and Industry 4.0
Description
Automate
Manufacture
ML
Big Data
Additive
2019
Data fusion for industrial prognosis [9]
Yes
Yes
Yes
No
No
2013
Recommendations for Industry 4.0 [10]
No
No
No
No
No
2014
Industry 4.0 in Europe [11]
No
No
No
No
No
2018
Industrial IoT in IoT Systems [12]
No
No
Yes
No
No
2015
Future of Manufacturing [13]
No
Yes
No
No
No
2017
Industry 4.0 Technologies [14]
No
No
No
Yes
No
2017
IIoT and Cyber Manufacturing [15]
No
Yes
No
Yes
No
2016
Complex View of Industry 4.0 [16]
No
No
No
No
No
2016
Cyber-Physical Systems Foundations [17]
No
No
No
No
No
2013
Recommendations for Industry 4.0 [18]
No
Yes
No
No
No
NA
Proposed work
Yes
Yes
No
Yes
-p
ro
of
Year
re
Yes
lP
By addressing gaps in existing research and highlighting areas of innovation, our survey aspires to provide
na
a forward-looking view that can guide future developments in the domain of IIoT.”
2. Taxonomy and framework for sensor fusion
ur
Wireless Sensor Networks (WSNs) find extensive application in various domains, notably in environmental monitoring as shown in Fig. 3. These networks consist of small, autonomous sensor nodes equipped
Jo
with sensors to collect data on environmental parameters such as temperature, humidity, air quality, and
more. WSNs are deployed in remote or inaccessible areas where traditional wired systems are impractical.
For instance, in agriculture, WSNs help farmers monitor soil moisture levels to optimize irrigation, reduce
water wastage, and enhance crop yields. In wildlife conservation, these networks enable the tracking of
animal movements and habitat conditions. Additionally, WSNs are vital in disaster management for early
detection of natural calamities like forest fires or earthquakes, allowing for timely response and mitigation.
The versatility and real-time data collection capabilities of WSNs make them invaluable tools in addressing
pressing environmental and societal challenges. Sensor fusion can be accomplished in several ways, with the
chosen method depending on the fusion process and the nature of the data being combined. Examples of
standard methods for fusing data from multiple sensors include: Using data fusion, which entails compiling
raw data from various sensors into a unified picture of the system or process under observation. Methods
like averaging, filtering, and interpolation may be used to combine the data [19].
In the realm of industrial applications, a diverse array of sensors plays a pivotal role in ensuring the efficiency, safety, and quality of various processes. These sensors, including temperature sensors (e.g., thermo7
ro
of
Figure 3: Naming convention of wireless sensors [20].
-p
couples, RTDs), pressure sensors (e.g., piezoelectric, capacitive), level sensors (e.g., ultrasonic, capacitance,
float), and flow sensors (e.g., turbine, magnetic, thermal flow meters), are crucial components of industrial
re
systems. They tirelessly monitor and control temperature variations, pressure levels, liquid and solid levels,
lP
and fluid flow rates. In addition, proximity sensors (e.g., inductive, capacitive) are instrumental in automation and robotics, while humidity sensors maintain precise humidity levels in controlled environments.
na
Vibration sensors detect machinery vibrations, and gas sensors (e.g., CO2, CH4) ensure workplace safety by
detecting the presence of specific gases. Position sensors (e.g., encoders), force sensors (e.g., load cells), light
ur
sensors (e.g., photodetectors, photodiodes), acoustic sensors (e.g., microphones, ultrasonic transducers), and
image sensors (e.g., CCD, CMOS) further extend the range of applications in industrial settings. Each of
Jo
these sensors serves a specific purpose, collectively contributing to the seamless operation and quality control
of industrial processes.
Features or characteristics extracted from data collected by multiple sensors are fused in the feature
fusion process rather than the raw data itself. The elements could be combined using various methods,
including decision trees, neural networks, and clustering algorithms. During the decision fusion phase, data
from multiple sensors and algorithms are combined into a single set of findings. Combining the results, such
as voting or weighting, may be used. In the end, the goals of the fusion process and the nature of the data
being fused will determine the precise approach to sensor fusion that will be taken. To get the best results,
employing more than one strategy may be necessary as shown in Fig. 3.
2.1. Sensor fusion in Industrial Internet of Things
The IIoT uses a method called ”sensor fusion” to increase the precision and dependability of collected
data by combining readings from multiple sensors. A complete picture of the systems and processes being
monitored is possible thanks to sensor fusion, which helps businesses get past the limitations of individual
8
sensors [21]. In a factory, for instance, sensor fusion could be used to get a fuller picture of a machine’s health
and performance by combining data from multiple sensors (such as temperature, pressure, and vibration)
[22]. Insights gained from this information could lead to the early detection of issues and the implementation
of fixes that would otherwise result in unscheduled downtime. It is especially helpful in industrial settings,
where multiple sensors may be used to monitor various parts of a system or process. To track variables
like temperature, vibration, and flow rate, sensors are commonly used in factories to track production line
efficiency [23]. The information gathered from these sensors can be used to understand better how the
production line is functioning as a whole, which can aid in locating issues and maximizing efficiency. A
ro
of
wide range of methods exists for actualizing sensor fusion. As opposed to feature fusion, which combines
the features or characteristics extracted from the data, sensor fusion combines the raw data from multiple
sensors [24].
Sensors are essential for diagnosing system health in data-driven enterprises, aiding prognosis through
-p
intelligent signal processing [25]. Industry 4.0 relies on sensors and AI for improved efficiency. Research shows
re
their potential in minimizing errors and enhancing productivity, but challenges like cost hinder widespread
adoption. Further studies are needed to address these issues and advance the field. This article explores
lP
modern methods for monitoring tool conditions during various machining operations [26]. Researchers are
focused on factors like cutting tool wear, cutting force, and surface roughness. Changes in these factors
na
impact dimensional accuracy and productivity significantly. Excessive wear can lead to tool breakage.
The study discusses using sensors and artificial intelligence (AI) for online monitoring. Sensors include
ur
dynamometers, accelerometers, and more. AI methods like neural networks, image recognition, and fuzzy
logic are examined. The article also addresses the strengths, weaknesses, and potential of these AI methods
Jo
for tool wear monitoring, as well as future directions for research in this field.
Sensor fusion is crucial for monitoring the cutting process effectively, ensuring longer tool life and highquality products [27]. The intricate nature of machining and variable interactions impact quality measures.
Employing multiple sensors facilitates comparing data from different sources, aiding decisions about tool and
workpiece conditions. An experimental study employed five sensors—measuring cutting forces, vibration,
acoustic emission, temperature, and current—during lathe turning of AISI 5140 with coated carbide tools.
Results show temperature and acoustic emission signals are about 74% effective in detecting flank wear.
Fuzzy logic-based prediction of wear using temperature and acoustic emission sensors demonstrates high
accuracy, suitable for sensor fusion. Instant tool breakage can be prevented using tangential and feed
cutting forces, acoustic emission, and vibration signals. Sensor fusion enhances reliability, robustness, and
consistency in machining processes.
With the help of decision fusion, data from various sources, such as sensors or algorithms, can be
incorporated into a single, comprehensive assessment. Different methods of sensor fusion include:
9
ro
of
Figure 4: Representation of sensor module internal entity [29].
-p
• Kalman filtering: It is a statistical method for estimating the state of a system based on a series of
noisy measurements. It can be used to combine data from multiple sensors and remove errors or biases
re
in the measurements [28].
lP
• Fuzzy logic: It is a form of artificial intelligence that can be used to process and interpret data from
multiple sensors and make decisions based on that data.
na
• Dempster-Shafer theory: It is a mathematical approach to combining information from multiple sources
ur
and estimating the probability that a particular event will occur .
The sensor fusion is an important technique in the IIoT, as it allows organizations to make more informed
Jo
decisions and optimize their operations based on a more accurate and comprehensive view of their systems
and processes.
2.2. Fault prediction using sensor fusion in IIoT
To predict when a piece of industrial equipment or a system will fail, researchers in the Industrial Internet
of Things (IIoT) combine data from multiple sensors. It can help businesses ensure continuous operation
with minimal chance of interruption. Fault prediction in the IIoT can be achieved through sensor fusion,
which involves the consolidation of data from multiple sensors to establish patterns and trends that may
indicate an impending failure [21]. It can aid businesses in avoiding unscheduled downtime and increasing
their overall uptime. To track variables like temperature, vibration, and flow rate, sensors are commonly
used in factories to track production line efficiency [30, 31, 32] . Over time, it may be possible to spot
patterns in the data collected by these sensors that point to the presence of an issue. Increases in vibration
or temperature, for instance, could mean that a machine needs to be serviced or fixed. Companies can use
several methods, such as machine learning algorithms, data analysis, and statistical modeling, to use sensor
10
fusion to predict faults. These methods can be used to spot anomalous data patterns and trends and then
notify the appropriate parties when an issue is found. Fault prediction by combining data from multiple
sensors is a viable technique in the IIoT, and it can be approached from many different angles [33, 34, 35].
One strategy is to run the collected sensor data through machine learning algorithms to look for trends
or patterns that might indicate an impending failure. By analyzing past data, these algorithms can be
taught to recognize anomalous behavior and then used to trigger warnings whenever a problem is suspected.
Alternatively, you could use statistical methods like probability distributions or statistical process control
(SPC) to keep an eye on the data coming in from the sensors and spot any unusual behavior that could
ro
of
point to a problem. When attempting to foresee problems, it is helpful to keep in mind a few general rules:
• Data collection: In order to predict faults, it is important to collect data from a variety of sources
such as sensors, machines, and systems. This data should be collected in real-time and should be
-p
representative of the system or process being monitored.
• Data analysis: Once the data has been collected, it is important to analyze it in order to identify
re
patterns and trends that may indicate a potential fault.
lP
• Modeling: In order to predict faults, it is often necessary to build a model of the system or process
being monitored. This model should be based on the data collected and should be able to accurately
na
predict the behavior of the system under different conditions.
• Validation: It is important to validate the accuracy of the fault prediction in order to ensure that it is
observations.
ur
reliable. This may involve testing the model on a sample of data and comparing the results to actual
Jo
200
The fault prediction using sensor fusion in the IIoT can help organizations to proactively identify and
address potential issues before they become major problems, enabling them to improve the reliability and
efficiency of their operations.
A sensor fusion solution for the IIoT involves a combination of hardware (sensors), software (data acquisition, storage, and analysis), and visualization tools that work together to enable organizations to make
better informed decisions based on the data collected from their sensors and devices [36, 37, 19] . There are
several key components that are often included in a sensor fusion solution for the IIoT:
1. Sensors: These are the devices that collect data from the environment. In the IIoT, sensors may be
used to monitor a variety of parameters, including temperature, vibration, flow rate, and more using
its internal module shown in 4. .
2. Data acquisition and transmission: This involves the process of collecting and transmitting the data
from the sensors to a central location for analysis. This may involve using wireless communication
technologies such as Wi-Fi or Bluetooth to transmit the data [38, 29] . .
11
3. Data storage and management: This involves the process of storing and organizing the data collected
from the sensors. This may involve using a database or data lake to store the data.
4. Data analysis: This involves the process of analyzing the data collected from the sensors in order to
identify patterns and trends that may be useful for predicting faults or optimizing performance. This
may involve using techniques such as machine learning, data mining, or statistical modeling to analyze
the data [39].
5. Visualization: This involves the process of presenting the results of the data analysis in a way that
is easy to understand and interpret. This may involve using visualizations such as graphs, charts, or
ro
of
maps to present the data.
6. Edge computing: Edge computing involves processing data at the edge of the network, close to the
source of the data. This can be an effective approach for sensor fusion in the IIoT, as it allows for
-p
real-time processing of data and can reduce the amount of data that needs to be transmitted over the
network [40, 20] .
re
7. Cloud computing: Cloud computing involves storing and processing data in a remote server or servers,
accessed over the internet. This can be a useful approach for sensor fusion in the IIoT, as it allows
lP
for the integration of data from multiple sources and can provide the necessary computing power and
storage capacity for large-scale data analysis [41, 42].
na
8. Distributed systems: Distributed systems involve dividing a complex system into smaller, more manageable components that can be distributed across different locations or devices. This can be an
ur
effective approach for sensor fusion in the IIoT, as it allows for the integration of data from multiple
Jo
sources and can improve the scalability and reliability of the system [43, 44].
In addition, there are many literature available which has made several classification . The existing
studies has taken the approach such as sensory fusion, industry 4.0, data fusion, machine learning approach,
fault diagnostics, sensor fusion challenges, and IIoT based solution [45, 46, 28, 47].
2.3. Adaptive Neuro-Fuzzy Inference System (ANFIS)
This artificial neural network has been developed to model the fuzzy logic systems used in many control
and decision-making systems, and the ANFIS-based sensor fusion has been presented in [48]. It can efficiently
learn and adapt to new data thanks to a combination of the strengths of artificial neural networks and fuzzy
logic systems. ”Fuzzy” rules, the foundation of fuzzy logic systems, are logical statements that can be either
true or false or even a grey area in between. A rule could be fuzzy if it said, ”If the temperature is hot,
then the air conditioning should be turned on.” Since ”hot” can mean different things to different people
in other contexts, this rule cannot be definitively proven true or false [49]. Fuzzy logic systems are wellsuited to dealing with this kind of subjectivity, and It combines its strengths with those of artificial neural
12
networks in learning. It employs a hybrid approach, using both fuzzy logic and neural network methods to
accomplish in this [50, 51]. It can be utilized in various settings, such as control systems, pattern recognition,
and prediction. Electrical engineering, computer science, and even medicine have all benefited from their
application.
Table 2: Brief description of existing sensor fusion, prognostics and application areas
Descriptive Approach, Ref
Objective
LAD [52]
Combination of controlled, manipulated and mea-
ro
of
sured flowrates
Outlier detection using EM [53]
Failures, environmental measures
Decision trees and fuzzy classifier [48]
Feature extraction and combination of data from
KDE and distance-based classification
-p
condition monitoring
Combining fastener features
re
[54]
Fusion of acoustic emissions and vibratory signals
DBN based on RBM [55]
Fusion of fault evidence and reason vectors
DWT and SVMs [56]
lP
Deep Random Forest and Bag Tree [49]
Multi-sensor fusion at feature level
Environmental indicators and production and wel-
na
Quantile regression forests [57]
fare parameters
ur
[58]
GPS and wind velocity sensors output
Jo
Framework based on hybrid adaptive
resonant theory ANN and adaptive
fuzzy inference [59]
Bayesian Inference [60]
Multistage data fusion at component and global
levels
FFT, kNN and K-means [61]
Fusion of vibration-based features from accelerometer data and locations of the monitored areas
2.4. Artificial Neural Networks (ANNs)
Sensor fusion is the process of combining data from multiple sensors to provide a more complete and
accurate understanding of a system or environment. In [62], an Artificial neural networks (ANNs) based
solution is proposed. It can be used for sensor fusion by training a neural network to integrate the data from
13
multiple sensors and make decisions or predictions based on the combined data. There are several ways in
which can be used for sensor fusion as following which is described in [57, 58]:
1. Feature extraction: It can be used to extract features from raw sensor data, such as identifying patterns
or trends in the data. This can help to improve the accuracy of sensor fusion by providing a more
structured and relevant representation of the data .
2. Data fusion: It can be used to fuse data from multiple sensors by combining the data in a way that
is more useful or meaningful. For example, an ANN could be used to fuse data from a camera and a
lidar sensor to create a more accurate 3D map of an environment .
ro
of
3. Decision making: It can be used to make decisions based on sensor fusion data, such as determining
the best course of action for a robot to take in a particular situation .
-p
2.5. Back Propagation Neural Networks ( BPNN)
In a solution based on artificial intelligence and feedforward neural networks, the BPNNs are employed
re
for sensor fusion [50]. It means they have a predetermined input layer, a hidden layer(s), and an output
lP
layer(s) [59]. The hidden layers use the input data to learn and extract features, and the output layer is where
the learned features are used to make predictions or decisions. In sensor fusion, it can aggregate information
from multiple sensors into a unified stream from which conclusions and predictions can be drawn. One way
na
to accomplish this is to train the BPNN using examples of sensor data from each sensor and the desired
output for each model [60]. It uses a training algorithm known as backpropagation. Its many applications
ur
in sensor fusion span robotics, autonomous vehicles, and industrial automation. They shine in situations
Jo
where it’s necessary to combine information from sensors with unique strengths and weaknesses, such as
when making a 3D map of an area using data from a camera and a lidar sensor.
2.6. Deep Belief Networks ( DBN )
When combining data from multiple sensors, artificial neural networks constructed using DBNs have
been shown to be effective. For DBNs to learn and derive features from the input data, they are constructed
with many tiers of interconnected “belief” units at multiple levels [55]. Each subsequent layer of belief units
builds upon the information it has received from the layer below it, eventually learning very generalized
characteristics as a result. DBNs can be used for sensor fusion because they can be trained with examples
of sensor data taken from multiple sensors, along with the desired output for each training example [63].
Regarding sensor fusion, the fact that DBNs can learn features from the data independently without the
assistance of labels gives them an advantage over supervised learning methods [64]. This can be helpful
in sensor fusion applications, where the expected result needs to be clarified or known. DBNs have been
used for various sensor fusion tasks, some of which include the recognition of objects, the comprehension of
14
scenes, and the detection of anomalies [65]. They have also been utilized in these applications to integrate
data from several different sensors, as is the case in robotics and autonomous vehicles.
2.7. Discrete Wavelet Transformation ( DWT )
The DWT is a tool that can analytics and extract features from it. The “wavelets” are oscillating
functions with a limited duration that are used to represent the signal at different scales [56]. The method
works by decomposing a signal or data series into a series of “wavelets”. In sensor fusion, discrete wavelet
transform can be used to extract features from the data produced by multiple sensors and combine them
in a meaningful way. It can be accomplished by combining the data with a discrete wavelet, which can be
ro
of
achieved by applying DWT to the data from each sensor in turn and then combining the wavelets produced in
a manner that is pertinent to the activity currently being performed. When it comes to sensor fusion, one of
the benefits of DWT is that it can provide a multiscale representation of the data. This data representation
-p
can be helpful when trying to identify patterns or features at different scales. In addition, DWT can be
re
applied to the data to de-noise or filter it by lessening the impact of interference or noise on the data.
The application can be used in a wide variety of sensor fusion tasks, such as processing images and videos,
lP
speech recognition, and medical imaging [66, 67]. In addition, it has been developed in robotics, self-driving
na
vehicles, and other applications where integrating data from a variety of sensors is essential.
2.8. Expectation Maximization (EM )
ur
The fault diagnosis method, known as expectation maximization (EM), is utilized in settings with hidden
or latent variables. The purpose of fault diagnosis is to determine whether a problem or failure exists within
Jo
a system by examining data obtained from sensors or other sources. An iterative algorithm estimates the
hidden or latent variables in a design by maximizing the likelihood of the observed data given the estimated
latent variables [53]. The algorithm begins with an initial estimate of the latent variables. Then it alternates
between two steps: the expectation step, in which it estimates the expected value of the latent variables
given the current estimate, and the maximization step, in which it evaluates the importance of the latent
variables that maximize the likelihood of the observed data. The algorithm begins with an initial estimate
of the latent variables [68]. Then it begins alternating between two steps: the expectation step, in which it
estimates the expected value of the latent variables given the current estimate, and the maximization By
training the algorithm on a dataset that contains examples of sensor data and fault labels, it can be utilized
for fault diagnosis. This process is known as ”training.” During the training phase, the algorithm acquires
the knowledge necessary to comprehend the connection between the sensor data and the latent variables
that stand in for the malfunctioning state of the system. After it has been trained, the algorithm will utilize
the sensor data to estimate the latent variables and make predictions regarding the fault state of the system.
It has been utilized in various fault diagnosis applications, such as diagnosing faults in industrial systems,
15
electrical power systems, and aircraft systems, amongst others. It has the advantage of handling missing or
incomplete data, which is helpful in fault diagnosis tasks because it is common for missing or incomplete
data.
2.9. Exponentially Weighted Moving Average (EWMA)
The exponentially weighted moving average (EWMA) statistical analysis method is presented for data
fusion. In computer systems that have time-series data, it can be utilized for fault diagnosis [69]. By
analyzing data obtained from sensors and other sources, the fault diagnosis process seeks to determine the
underlying cause of a problem or failure detected in a system. It is a method for estimating the underlying
ro
of
trend or mean of a time-series data series. In this method, more weight is given to data collected recently,
and less importance is given to data collected for longer [70]. The weighting factor used in EWMA is
exponentially decreasing, which means that the weight of each data point decreases exponentially as the
-p
data point gets older, and it is because the weighting factor itself is exponentially decreasing. It has been
re
used for fault diagnosis by analyzing the difference between the observed data and the estimated mean or
trend. This can be done in either direction. If the difference is significant enough to cross a certain threshold,
lP
it may suggest an error or an anomaly somewhere within the system. EWMA can also be utilized to identify
changes in the mean or trend of the data, both of which can serve as additional indicators [71]. It has
na
been utilized in many fault diagnosis applications, such as industrial system fault diagnosis, electrical power
system fault diagnosis, and aircraft system fault diagnosis. It is beneficial for detecting gradual changes in
ur
the mean or trend of the data, which may not be detectable using other methods. This can be the case
Jo
because it is easy to miss these changes.
2.10. Fast Fourier Transform ( FFT )
The fast Fourier transform (FFT) is an algorithm that can compute a data sequence’s discrete Fourier
transform (DFT) [72]. The discrete Fourier transform (DFT) is a mathematical operation that can be
used to analyze the frequency content of a signal. It takes a signal or data series and breaks it down into
component frequencies and amplitudes [73]. The fast Fourier transform (FFT) is a method for computing
the discrete Fourier transform (DFT), which is essential in various applications that require real-time data
processing. The Fast Fourier Transform is based on dividing a long data sequence into several shorter
rows and then computing each individual shorter sequence’s Discrete Fourier Transform (DFT) [61]. This
makes it possible for the Fast Fourier Transform (FFT) to calculate the DFT of a data sequence much more
quickly than other methods, such as the direct computation of the DFT. Incorporating areas such as signal
processing, image processing, and communication systems is beneficial. Analyzing the frequency content of
signals that change over time, such as audio signals or time-series data, is a beneficial application for this
technique. FFT is utilized in many different practical applications in addition to its use in analysis. Some
16
examples of these applications include data compression, audio, and image synthesis, and digital filtering
[74].
By applying the fast Fourier transform (FFT) to the data from multiple sensors and combining the
frequency spectra generated as a result in a manner pertinent to the task at hand, the fast Fourier transform
(FFT) can be used for sensor fusion [75, 76, 77, 78, 79]. In sensor fusion, FFT has been used to extract
features from the data produced by multiple sensors and combine them in a meaningful way. It is done to
improve the accuracy of the overall data and can be accomplished by applying FFT to the data from each
sensor in turn and then combining the frequency spectra produced in a manner that is pertinent to the
ro
of
task being performed. The FFT-based sensor fusion is a concept that is proposed in. One of the benefits of
utilizing FFT for sensor fusion is that it can provide a representation of the data that is condensed while still
being informative. This data representation can help locate patterns or features within the data. FFT can
also be used to de-noise or filter the data, which can improve the accuracy of sensor fusion by reducing the
-p
effect of noise or interference on the data. FFT can be used in a variety of other contexts as well. The fast
re
Fourier transform (FFT) has found use in a wide variety of sensor fusion applications, such as processing
images and videos, speech recognition, and medical imaging. In addition, it has been utilized in robotics,
lP
self-driving vehicles, and other applications where integrating data from a variety of sensors is essential.
na
2.11. Functional Principal Component Analysis ( FPCA )
Fault diagnosis in systems with operational data can be accomplished through functional principal com-
ur
ponent analysis, also known as FPCA. The method was used for fault diagnosis to decompose operational
data into a set of main components. Principal components are orthogonal functions that capture the most
Jo
critical aspects of the data [80]. The principal components are ranked in order of importance, with subsequent principal components explaining progressively less and less variance [81]. In, FPCA has been utilized
for fault diagnosis. This is accomplished by analyzing the principal components of the available data to
spot any changes or anomalies in the data. For instance, if the first principal component suddenly alters its
shape or magnitude significantly, this might suggest a problem with the system [82]. Additionally, it can
be utilized to determine whether or not there have been any shifts in the general structure of the data [83].
It can perform various fault diagnosis tasks, such as fault diagnosis in industrial systems, electrical power
systems, and aircraft systems, among other techniques. It is constructive for analyzing operational data,
which includes data that varies over time or in different locations.
2.12. Gaussian Mixture Models ( GMM )
The GMMs is a probabilistic model for sensor fusion that can be utilized [84] . In this context, the GMMsbased solution on the idea of representing data distribution as a mixture of several Gaussian distributions
is proposed [85]. This solution is based on the idea that the data can be represented as a distribution.
17
In the data, every one of the distributions represents a different component or cluster. In the context of
sensor fusion, GMMs can combine the data from multiple sensors into a single set of data and then use that
fused data to make predictions or make decisions. To accomplish this, the GMMs are trained on a dataset
containing examples of sensor data from multiple sensors and the desired output for each instance. This
allows the GMMs to learn how to produce the desired results [86]. It understands the relationships between
the sensor data and the desired output during training. It can then be used to make predictions or decisions
based on new sensor data based on what they have learned during training. It is useful for sensor fusion
because it can handle data with complex distributions, such as multi-modal data or outliers. This is one of
ro
of
the advantages of using GMMs. It can also be used to model uncertainty in the data, which can be helpful
in situations where the data is noisy or unreliable. It has been utilized in various sensor fusion tasks, such as
anomaly detection, speech recognition, and object recognition. In robotics, autonomous vehicles, and other
-p
applications where the integration of data from multiple sensors is essential, they have also been utilized.
re
2.13. Gaussian–Bernoulli Restricted Boltzmann Machines ( GRBMs)
For the Internet of Things (IoT), the GBRBM-based sensor fusion was proposed in [87]. It is an artificial
lP
neural network capable of handling the data produced by sensor fusion. RBMs are a type of probabilistic
generative model that can learn to reconstruct data from a hidden representation. This particular model
na
is a variant of restricted Boltzmann machines (RBMs), also a type of RBM. It have been used in sensor
fusion to combine data from multiple sensors to make predictions or decisions based on the combined data
ur
[88, 89]. To accomplish this, the GBRBMs are trained on a dataset containing examples of sensor data
from multiple sensors and the desired output for each model [50]. This allows to learn how to produce the
desired results. During training, its learns the relationships between the sensor data and the desired output.
Jo
400
These learned relationships can then be used to make predictions or decisions based on the new sensor data.
When it comes to sensor fusion, one of the benefits of using is that they can learn complicated relationships
between the data collected by the sensors and the desired output, even if those relationships are nonlinear or
challenging to model using other approaches. It can also be utilized to model uncertainty in the data, which
can be helpful in circumstances in which the data is noisy or unreliable. It have been utilized in performing
a wide variety of sensor fusion tasks, some examples of which include object recognition, speech recognition,
and anomaly detection. In robotics, autonomous vehicles, and other applications where the integration
of data from multiple sensors is essential, they have also been used [90, 91, 92] . For fault prediction in
systems containing time-series data, Gaussian-Bernoulli-restricted Boltzmann machines, have been utilized.
The purpose of fault prediction is to determine how likely a fault will occur in a given system based on
information gathered from sensors and other sources. To make use for fault prediction, the model would
first need to be trained on a dataset containing illustrative examples of sensor data and fault labels. It would
acquire this knowledge about the relationships between the sensor data and the fault’s presence or absence
18
while being trained. After they have been trained, the GBRBMs can be used to predict the likelihood of a
fault occurring based on the latest sensor data.
The advantage of using it for fault prediction is that they can learn complicated relationships between
the sensor data and the fault state of the system, even if those relationships are either challenging to model
using other methods or are nonlinear [93]. It can also be utilized to model uncertainty in the data, which
can be helpful in circumstances in which the data is noisy or unreliable. It has been used in a wide variety of
fault prediction applications, such as the prediction of faults in industrial systems, electrical power systems,
and aircraft systems, amongst other applications. They have the advantage of dealing with large amounts of
ro
of
data and learning complex relationships between the data and the fault state of the system. Both of these
capabilities give them an advantage.
2.14. General Regression Neural Network (GRNN)
-p
Artificial neural networks, also known as general regression neural networks (GRNNs), are one type of
re
neural network that can be utilized for the process of sensor fusion [50]. GRNNs are a subclass of neural
networks known as radial basis function (RBF) networks. RBF networks are a form of feedforward neural
lP
network that has the ability to learn to approximate nonlinear functions. GRNNs can be utilized in sensor
fusion to combine data from multiple sensors to make predictions or decisions based on the combined data.
na
To accomplish this, GRNNs are trained on a dataset containing examples of sensor data from various sensors
and the desired output for each measure. This allows the GRNNs to learn how to produce the desired results.
ur
The GRNNs understand the relationships between the sensor data and the desired output during training.
These learned relationships can then be used to make predictions or decisions based on the newly collected
Jo
sensor data. The advantage of using GRNNs for sensor fusion is that they can learn to approximate complex
nonlinear functions. This can be helpful for tasks where the relationships between the sensor data and the
desired output need to be more well-defined or are challenging to model using other methods [94, 95, 96, 97].
GRNNs can also be used to model uncertainty in the data, which can be helpful in circumstances in which
the data is noisy or unreliable. GRNNs can be used to model uncertainty in the data. Object recognition,
speech recognition, and anomaly detection are some application areas that are being explored. In robotics,
autonomous vehicles, and other applications where the integration of data from multiple sensors is essential,
they have also been used.
2.15. Hidden Markov Model ( HMM )
The Hidden Markov Model (HMM) is a statistical model that is used to represent a sequence of observations that are generated by a process with hidden or latent states. This sequence of words is used to conclude
the process. Natural language processing, speech recognition, and computer vision are just some areas that
extensively use hidden Markov models (HMMs) [73]. In a hidden Markov model (HMM), it is assumed that
19
the latent states of the process are Markovian. The probability of transitioning to a new state depends only
on the current state and not on the states that came before it. It is generally assumed that a Gaussian or
multivariate Gaussian probability distribution applies to the situation when determining whether or not the
observations are considered to be generated by the latent states. Modeling a wide variety of phenomena,
such as time series, sequence, and multivariate data can be accomplished by utilizing HMMs. When it
comes to tasks in which the relationships between the observations and the latent states are not well-defined
or are challenging to model using other methods, they are instrumental. They are beneficial for tasks like
these. HMMs are frequently used for activities such as classification, clustering, and prediction. These are
ro
of
all endeavors in which the objective is to learn the underlying structure of the data to then base decisions
or projections on the network that was discovered. They are also useful for activities such as decoding and
estimation, both of which involve making inferences about the hidden states of a process based on the data
that has been observed.
-p
In computer systems containing time-series data, the HMMs can be utilized for fault prediction. The
re
purpose of fault prediction is to determine how likely a fault will occur in a given system based on information
gathered from sensors and other sources. The HMM would need to be trained on a dataset containing
lP
examples of sensor data and fault labels for it to be used for fault prediction. The HMMs would acquire this
knowledge about the relationships between the sensor data and the fault’s presence or absence while being
na
trained. After the HMMs have been trained, they can be used to make predictions about the likelihood of
a fault occurring based on the current state of the sensor data. HMMs can learn the underlying structure
ur
of the data and model the relationships between the sensor data and the fault state of the system. It gives
them an advantage when it comes to fault prediction. HMMs can also be used to model uncertainty in the
Jo
data, which can be helpful in situations where the data is noisy or unreliable. HMMs can also be used to
model uncertainty in the data. In addition, it has been utilized in various fault prediction applications, such
as predicting faults in industrial systems, electrical power systems, and aircraft systems, amongst others.
2.16. k-Nearest Neighbors ( kNN )
In the fault prediction field in systems containing time-series data, the k-NN-based machine learning
algorithm has also been very successful. The purpose of fault prediction is to determine how likely a fault
will occur in a given system based on information gathered from sensors and other sources [98]. The knearest neighbor (k-NN) algorithm needs to be trained on a dataset containing examples of sensor data and
fault labels before it can be used for fault prediction. The algorithm would understand the relationships
between the presence or absence of a fault. After it has been trained, the algorithm can be used to predict
the likelihood of a responsibility occurring based on the data from newly installed sensors. For the algorithm
to produce a forecast, it must first identify the k data points closest to one another by utilizing a distance
metric. After that point, the prediction would be based on the typical educational background of the k20
neighbors who are located the nearest to you. It is simple to construct and can work with many different
kinds of data in various contexts. This is the aspect that makes it stand out the most. It is beneficial for
tasks in which the links between the data and the fault state of the system need to be better defined or are
challenging to characterize using other approaches. This type of work is especially suited to this method.
This class of pursuits encompasses a wide variety of issues in their entirety.
2.17. Kernel Density Estimator ( KDE )
Calculating a random variable’s probability density function (PDF) can be done using a non-parametric
technique known as KDE. The probability density function, or PDF, is a function that can describe the
ro
of
likelihood of a random variable taking on a variety of values. Smoothing out a collection of data points
with the help of a kernel function is the basis for the KDE technique, which is used to estimate the PDF
of a random variable. The kernel function is responsible for determining the smoothing curve’s shape and
-p
the influence each individual data point has on the final PDF. It is beneficial for estimating the PDF of a
re
random variable when the distribution of the variable is unknown or when the sample size is small. These
circumstances make it challenging to obtain a large enough data set [54]. Additionally, it can be utilized
lP
for activities such as clustering, which aims to recognize patterns or groups hidden within the data. Kernel
density estimators, also known as KDEs, have been used for sensor fusion. These estimators are applied to
na
the data from multiple sensors, and then the resulting density estimates are combined in a manner pertinent
to the task at hand. KDE’s can be utilized in the context of sensor fusion to extract features from the
ur
data produced by multiple sensors and combine them in a meaningful manner. This can be accomplished
by combining the data in a meaningful way by first applying KDE’s to the data from each sensor in its
Jo
own right and then combining the density estimates obtained in a manner pertinent to the task currently
being performed. The key advantages of sensor fusion are that it can provide a smooth and informative
representation of the data, which can help recognize patterns or features in the data, and that it can reduce
the amount of noise in the data. KDE’s can also be used to denoise or filter the data, which can improve
the accuracy of sensor fusion by reducing the effect of noise or interference on the data. KDE’s can be
used to denoise or filter the data. KDEs are used for various tasks related to sensor fusion, such as speech
recognition, image and video processing, and medical imaging. In robotics, autonomous vehicles, and other
applications where the integration of data from multiple sensors is essential, they have also been used.
2.18. Logical Analysis of Data ( LAD )
The Latent Attribute Model (LAD) is a statistical method that can be applied to data to recognize
patterns or relationships. The logic-based approach to data analysis (LAD) is predicated on employing logical
rules or formulas to describe the relationships between variables present in the data and then employing
these rules to make predictions or decisions based on the data. Statistical analysis, machine learning, and
21
data mining are just some of the many fields that can use LAD’s diverse applications. It is constructive for
tasks where the relationships between the variables in the data are complex or not well-defined and where
traditional statistical methods may not be effective. One of the most common applications of this method
is in the field of finance [52]. To carry out LAD, a collection of logical rules or equations must be applied
between the variables. After that, these rules are applied to the data to determine any patterns or trends
that may exist. In the situation line classification, regression, and clustering processes, LAD helps learn
the underlying structure so that one can make predictions or decisions based on the network that has been
learned. It has been demonstrated to be effective at identifying patterns and relationships in data that
ro
of
may not be detectable using other methods, making it useful for data analysis in finance, marketing, and
healthcare, among others.
2.19. Local Outlier Factor (LOF)
-p
The level of unexpectedness, also known as the LOF, is a measurement that determines an object in a
re
dataset that can be considered an outlier [53]. When applied to the data from multiple sensors, LOF can be
utilized for sensor fusion by combining the relevant outlier scores produced from applying it to that data. In
lP
the context of sensor fusion, localization-based feature extraction (LOF) has been used to extract features
from the data produced by multiple sensors and combine them in a meaningful way. It allows for the data
na
to be used more holistically to accomplish by applying LOF to the data from each sensor in turn and then
combining the outlier scores that were generated as a result. When it comes to sensor fusion, one of the
ur
benefits of utilizing LOF is that it can provide a condensed and informative representation of the data. This
representation of the data can help locate unusual or unexpected patterns or features within the data. The
Jo
data can be denoised or filtered using LOF, which can improve the accuracy of sensor fusion by reducing
the effect of noise or interference on the data. LOF can also be used to improve the accuracy of the data.
The tasks associated with sensor fusion include the detection of anomalies, the prevention of fraud, and the
strengthening of cybersecurity. In addition, it has been utilized in robotics, self-driving vehicles, and other
applications where integrating data from a variety of sensors is essential.
2.20. Principal Component Analysis (PCA )
PCA is based on finding a set of orthogonal linear combinations of the variables in the dataset, which
capture the essential characteristics of the data [99]. This can be done by using the variables in the dataset
to create linear combinations. The complexity of the data can be reduced by finding patterns or trends in
the data, or it can be used to simplify the data by removing information that is redundant or irrelevant. It is
beneficial for tasks where the relationships between the variables in the data are complex or not well-defined
and where traditional statistical methods may not be effective. One of the most common applications of
this method is in the field of finance. The principal component analysis (PCA) was carried out on the data
22
after it had been standardized [100]. It followed that the covariance matrix of the standardized data was
computed. After that, the principal components are obtained by projecting the data onto the eigenvectors.
The first principal component explains the most variance in the data, and subsequent components define
an increasingly smaller amount of variance with each passing detail. It has been shown to be effective at
reducing the complexity of the data when combined with other methods applied to a wide variety of tasks,
such as data visualization, feature extraction, and data compression.
2.21. Restricted Boltzmann Machines ( RBM )
A specific variety of ANNs known as RBMs is applied to the task of learning a probabilistic model of a
ro
of
dataset. RBMs are a generative model, which means they can learn how to reconstruct data from a hidden
representation. This ability is what distinguishes generative models from other types of models. RBMs are
made up of two units: a visible layer that stands in for the data fed into the model and a hidden layer that
-p
stands in for the variables that aren’t immediately apparent from the data. A set of weights, which define
re
the relationships between the visible and hidden units, are connected to the units in both the visible and
the hidden layers. These weights connect the units in both layers. To train an RBM, the model must first
lP
be provided with a set of input data, after which the weights must be modified to ensure that the highest
possible probability is achieved for the data being generated by the model. They are effective when applied
na
to a wide variety of tasks, including feature learning, dimensionality reduction, and data generation, and
have been shown to be applied to a wide variety of functions [55].
ur
For fault prediction in systems containing time-series data, restricted Boltzmann machines (RBMs) have
been utilized. The RBM would need to be trained on a dataset containing examples of sensor data and fault
Jo
labels for it to be used for fault prediction. During training, the RBMs would figure out the connections
between the data collected by the sensors and the presence or absence of a problem in the system. After
they have been trained, RBMs can be used to make predictions about the likelihood of a fault occurring
based on the data coming from new sensors. The ability of RBMs to train complex relationships between the
sensor data and the fault state is one of the benefits of using RBMs for fault prediction. These relationships
can be nonlinear or challenging to model using other methods, but RBMs can easily handle both of these
challenges. RBMs can also be used to model uncertainty in the data, which can be helpful in situations
where the data is noisy or unreliable. RBMs can be used to model uncertainty in the data. It is helpful for
tasks involving fault prediction, such as fault prediction in industrial systems, electrical power systems, and
aircraft systems, among other techniques.
2.22. Recurrent Neural Networks ( RNN )
The RNNs are a type of artificial neural network that is well-suited to modeling time-series data and
sequential data [101]. RNNs are equipped with a feedback loop or recurrence, which enables them to process
23
data sequentially. Data processing happens one element at a time to keep a memory state of the processed
data. In the context of sensor fusion, recurrent neural networks (RNNs) can be utilized to combine data from
multiple sensors to derive conclusions or make decisions based on the combined data. RNNs are trained on a
dataset containing examples of various sensor data, as well as the desired output for each model. This allows
the RNNs to accomplish the aforementioned goal. The RNNs learn the relationships between the input data
and the expected output during training. These learned relationships can later be used to make predictions
or decisions based on new sensor data. RNNs can learn complex relationships between the sensor data and
the desired output, even if those relationships are nonlinear or challenging to model using other methods,
ro
of
which is one advantage of using RNNs for sensor fusion. Another advantage is that RNNs can learn complex
relationships between the sensor data and the desired output. RNNs can also be used to model uncertainty
in the data, which can be helpful in circumstances in which the data is noisy or unreliable. RNNs can be
used to model uncertainty in the data. RNNs are used for various tasks in sensor fusion, including speech
-p
recognition and time series prediction. It has been put to use in robotics, autonomous vehicles, and other
re
applications where it is necessary to integrate data from several different sensors.
lP
2.23. Seasonal Autoregressive Moving Average ( SARMA )
A statistical model called SARIMA is utilized to make projections regarding time-series data that demon-
na
strates seasonality [69]. This model is a generalization of the moving average (MA) model and the autoregressive model (AR). The models are defined by three parameters: the autoregressive (AR) order, the moving
ur
average (MA) order, and the seasonal (S) order. These three orders are listed in the following order:
• The number of lags of the time-series data that are used in the model is determined by the AR order.
Jo
600
• The MA order determines the number of lags of the forecast error that are used in the model.
• The S order determines the number of lags of the time-series data that are used to model the seasonal
component of the data.
These three orders are listed in the following order: AR, MA, and S. In most cases, this is accomplished
through the utilization of an optimization algorithm such as gradient descent, which works to reduce the
gap between the actual data and the data that was predicted. The SARIMA modeling approach is useful for
various applications, including time-series prediction, trend analysis, and forecasting. They have been shown
to be effective at predicting the future values of time-series data, which is particularly useful for modeling
time-series data that exhibit seasonality. They are particularly useful for modeling time-series data that
exhibit seasonality. For fault prediction in systems with time-series data that demonstrates seasonality, the
SARIMA models have been put to use. The purpose of fault prediction is to determine the likelihood that a
fault will occur in a given system based on information gathered from sensors and other sources. To make use
24
of SARIMA models for fault prediction, the model would first need to be trained on a dataset containing
illustrative examples of sensor data and fault labels. The SARIMA model would learn the relationships
between the sensor data and the presence or absence of a fault, taking into account the seasonality of the
data while it was being trained. This would take place during the training process. After it has been trained,
the SARIMA model can be used to make predictions about the likelihood of a fault occurring based on the
data coming from new sensors. One of the benefits of using SARIMA models for fault prediction is that
they can model the seasonality of the data. It can be helpful in situations involving systems where the
probability of a fault occurring may change over time due to factors such as the season. It is also possible
ro
of
to use SARIMA models to model uncertainty in the data, which can be helpful in circumstances where the
data are noisy or unreliable.
2.24. Similarity Based Modeling ( SBM )
-p
Modeling that is based on similarities between data points and uses similarity measures to compare the
re
data points and draw conclusions or make predictions as a result of those similarities is called similaritybased modeling [69]. A similarity measure is used in similarity-based modeling to compute the degree of
lP
similarity between two sets of data points based on the features or characteristics that each group of data
points possesses. Any function that can quantify the degree to which the data points are similar can be
na
used as the similarity measure. It is helpful for tasks in which the relationships between the variables in the
data are complex or not well-defined, and the use of traditional statistical methods may not be effective.
ur
For sensor fusion, similarity-based modeling has been carried out here. It models a set of data points as the
first data points collected, then extracts the features or characteristics of the data points. After that, the
Jo
similarity measure is used to compute the similarity between each pair of data points, and the similarities
that are computed are then used to make forecasts or judgments based on the data. When the goal is to
learn the underlying structure and then make predictions or decisions based on the learned configuration,
similarity-based modeling can be used to accomplish both goals.
2.25. Self-Organizing Map Minimize Quantization Error (SOM-MQE)
Unsupervised learning and data visualization are two of the most common applications for self-organizing
maps (SOMs), a type of artificial neural network [99]. SOMs are trained by adjusting the weights of the
connections between the units to minimize the quantization error that exists between the data that is input
and the information that is output. SOMs are composed of a set of units or nodes that are arranged in a
grid. SOMs can be utilized in the context of fault prediction to minimize the quantization error between
the sensor data and the fault labels present in a dataset [102]. SOMs would need to be trained on a dataset
containing examples of sensor data and fault labels to accomplish this goal. During the training process, the
SOMs would make any necessary adjustments to the weights of the connections between the units to reduce
25
the quantization error between the sensor data and the fault labels [103]. After they have been trained, the
SOMs can be used to make predictions about the likelihood of a fault occurring based on the data coming
from new sensors. SOMs can learn to represent the relationships between the sensor data and the fault labels
in a compact and informative manner, which can help identify patterns or features in the data relevant to
the task. This is one of the advantages of using SOMs for fault prediction. Another advantage is that SOMs
can learn to represent the relationships between the sensor data and the fault labels. SOMs can also be
used to visualize the data in a low-dimensional space, which can help understand the data’s structure and
identify patterns or trends in the data. SOMs can also be used to visualize the data in a high-dimensional
ro
of
space [104, 105, 106].
2.26. Support Vector Machines (SVMs)
SVMs are especially beneficial for use in situations where the relationships between the variables in
-p
the data are difficult to understand or poorly defined, and traditional statistical methods may need to
re
be more productive. As a result of their resistance to noise and ability to process high-dimensional data,
they are ideally suited for a wide variety of machine-learning applications [107, 108]. To train a support
lP
vector machine (SVM), one must first collect a dataset and then extract the features or characteristics of
the individual data points. The SVM algorithm is then applied to locate the hyperplane that separates the
na
various classes of data points to the greatest extent possible. It has been demonstrated that the applications,
which include classification, regression, and the detection of outliers, are practical for learning complex
ur
relationships in the data and making accurate predictions or decisions based on the data [109, 110].
Support vector machines, also known as SVMs, are machine learning algorithms that can be utilized for
Jo
fault prediction in settings where the data can be separated into distinct classes or categories. The purpose of
fault prediction is to determine how likely a fault will occur in a given system based on information gathered
from sensors and other sources. The SVM model would need to be trained on a dataset containing examples
of sensor data and fault labels for it to be used for fault prediction. During training, the SVM algorithm would
locate the hyperplane in the high-dimensional space defined by the sensor data that maximally separates
the different classes of data points (fault and no-fault). After the SVM model has been trained, it can be
used to predict the likelihood of a fault occurring based on new sensor data by determining which class the
data belongs to and which category it belongs to. The fault prediction is that they can learn complicated
relationships between the data and the state, even if the connections are nonlinear or challenging to model
using other methods. This is a significant advantage over other methods [111]. In addition, SVMs are
resistant to noise and can process high-dimensional data, both of which are essential characteristics for
many fault prediction tasks.
26
2.27. Vibration-based Condition Monitoring ( VCM )
Vibration-based condition monitoring offers several distinct benefits compared to more conventional
condition monitoring methods. One of the benefits is that it is possible to carry it out continuously, which
makes it possible to monitor the system’s state [112, 113, 114]. It is conducive for systems that are essential
or operate continuously because it enables the early detection of problems and timely implementation of
corrective action [61]. It is particularly useful for systems that are essential or run continuously [115].
Condition monitoring based on vibrations has several benefits, one of which is that it is non-invasive and
only moderately expensive. Compared to other condition monitoring methods, it does not require the
ro
of
system to be disassembled or shut down to perform the analysis, which can result in significant time and
financial savings. Because vibration-based condition monitoring is relatively simple to implement and can
be automated, it is ideally suited for various industrial and commercial uses [116, 117]. Manufacturing,
aerospace, and energy are the application areas, and some of the tasks that can be performed with this
-p
technology include fault diagnosis, performance optimization, and maintenance planning.
re
The fault diagnosis method, known as vibration-based condition monitoring, is utilized frequently in
various industries, including the manufacturing, aerospace, and energy industries. The purpose of fault
lP
diagnosis is to determine the nature of the problem or malfunction within a system by analyzing data
obtained from sensors and other sources [97, 118, 119]. In performing vibration-based condition monitoring
na
on a system to diagnose problems, sensors are typically installed to measure the vibrations produced by
the system. The vibration data is then analyzed using methods such as spectral analysis or time-frequency
ur
analysis to identify patterns or trends in the data that may indicate changes in the system’s condition.
These patterns or trends can be used to determine whether or not there has been a change in the system’s
Jo
state. After that, you can use the patterns or trends you found to diagnose the issue and figure out what the
primary source of the issue is. Vibration-based condition monitoring is an excellent choice for fault diagnosis
because it can be carried out continuously, enabling real-time monitoring of the system’s condition. It can
make vibration-based state monitoring an ideal choice. To carry out the analysis, the system does not
need to be taken apart or powered down, which means that it is relatively inexpensive and non-intrusive
[120, 121]. Vibration-based condition monitoring is an essential component of Industry 4.0, the fourth
industrial revolution. It is used to monitor the health and performance of machinery and equipment in
real-time by utilizing sensors and advanced analytics to detect changes in the vibrations produced by the
system. Industry 4.0 is intended to monitor the health and performance of machinery and equipment. This
enables the early detection of problems and timely corrective action, improving the system’s efficiency and
reliability. Moreover, early detection of the issues allows for timely disciplinary action. The optimization of
maintenance schedules and the planning of maintenance activities are both areas in which vibration-based
condition monitoring plays a role in Industry 4.0. It is possible to determine when maintenance will likely
be required by analyzing the data on vibration and then planning the maintenance activities following that
27
prediction. It could improve the system’s overall efficiency and reduce the time it is down.
Overall, vibration-based condition monitoring is an essential component of Industry 4.0 because it enables
the continuous monitoring of the health and performance of machinery and equipment, which can improve
the efficiency and reliability of the system. In addition, this type of monitoring reduces the risk of human
error and enhances safety. In addition, it is an essential tool for fault diagnosis in many industries. It is
because it enables the early detection of problems and the timely adoption of corrective action, which can
improve the system’s efficiency and reliability.
ro
of
Table 3: Brief description of existing sensor fusion, prognostics and application areas
Descriptive Approach, Ref
Objective
GRBMs [87]
Deep statistical feature learning from vibration
-p
measurements
Operational data
GMM, SOM-MQE and PCA [99]
CMS data and SCADA variables
Model-based reasoning [122]
Sensor fusion of real-time monitoring of system sta-
re
KDE and SVMs [111]
lP
tus
ANNs [62]
Operational parameters, environmental conditions
na
and the electric energy consumption of the alternator
ur
SBM [69]
Operational parameters and environmental condi-
tions
Vibration and current signals
GRNN, BPNN and ANFIS [50]
Operating parameters (static data) fused with as-
Jo
Rule-based fuzzy semantic inference [63]
set (i.e. current, load and temperature) and fault
conditions
Interval-valued fuzzy reasoning [65]
Combination of the system performance decay
PCA and kNN [100]
Multiple sensors fusion at accelerometer and load
cell data
3. Implementation challenges in sensor fusion
Sensor fusion involves combining data from multiple sensors to provide a more accurate and comprehensive understanding of the environment or system being monitored. There are several challenges that can
28
arise when implementing sensor fusion:
1. Data alignment: Sensors may not be perfectly synchronized, which can make it difficult to accurately
align the data from different sensors.
2. Data uncertainty: Sensors have varying levels of accuracy and precision, which can make it difficult
to combine the data in a meaningful way.
3. Sensor failure: Sensors can fail or malfunction, which can impact the accuracy of the sensor fusion
system.
4. Complexity: Sensor fusion systems can be complex, especially when sensors are different in nature
ro
of
and have multiple sources of data. This can make it difficult to design and implement the system
effectively.
5. Real-time performance: In many applications, sensor fusion systems must operate in real-time, which
-p
can be challenging due to the computational demands of processing and combining data from multiple
sensors.
re
6. Limited resources: Sensor fusion systems often require significant computational resources, which can
lP
be a challenge in resource-constrained environments.
7. Data privacy: In some cases, sensor fusion systems may involve processing sensitive data, which can
na
present challenges in terms of data privacy and security.
3.1. Future Scope and Research Directions
ur
In this section, we elaborate on the future scope and potential research directions in the field of machine
Jo
learning-based sensor fusion techniques in the Industrial Internet of Things (IIoT). We aim to provide
extensive discussions and insights into the emerging trends and opportunities for further advancements in
this domain.
1. Emerging Trends in Machine Learning-based Sensor Fusion: The field of machine learning-based sensor
fusion in the IIoT is witnessing rapid advancements and evolving trends. To gain a comprehensive
understanding of the recent developments, The study conducted a statistical analysis of the literature
in this area. We collected relevant data from reputable academic databases, conference proceedings,
and scholarly publications to identify key trends and patterns. Our analysis revealed a significant
increase in the number of publications and research activities related to machine learning-based sensor
fusion techniques in recent years. This growth signifies the growing interest and recognition of the
potential of these techniques in addressing the challenges of data integration and advanced analytics
in IIoT systems.
2. Research Focus Areas: Based on our analysis, the study identified several research focus areas that
are gaining prominence in the field:
29
• Enhanced Sensor Integration: Researchers are exploring novel approaches to effectively integrate
data from diverse sensors in IIoT systems. This includes techniques such as deep learning-based
fusion, probabilistic modeling, and information fusion algorithms to improve the accuracy and
reliability of sensor data fusion.
• Edge Computing and Distributed Fusion: With the proliferation of edge computing in IIoT,
there is a growing interest in developing distributed sensor fusion algorithms that can leverage
edge devices’ computational capabilities. This enables real-time processing, reduces latency, and
enhances the scalability of sensor fusion in large-scale IIoT deployments.
ro
of
• Security and Privacy: As IIoT systems handle sensitive data, ensuring robust security and privacy
measures is crucial. Researchers are focusing on developing secure and privacy-preserving sensor
fusion techniques that protect sensitive information while maintaining the accuracy and integrity
-p
of fused data.
3. Emerging Topics: Our analysis also revealed several emerging topics that show promising potential for
re
future research in machine learning-based sensor fusion in IIoT:
lP
• Federated Learning: The application of federated learning techniques in sensor fusion allows collaborative model training across distributed sensors while preserving data privacy. This emerging
ments.
na
area holds great promise for improving sensor fusion performance in decentralized IIoT environ-
ur
• Explainable AI: With the increasing complexity of machine learning models, there is a growing
Jo
need for explainable AI techniques in sensor fusion. Researchers are exploring methods to provide
interpretable fusion results, enabling stakeholders to understand the decision-making process and
build trust in the system.
• Adaptive Fusion Algorithms: As IIoT systems operate in dynamic environments, adaptive fusion
algorithms that can dynamically adjust their parameters and fusion strategies based on changing
conditions are gaining attention. This area presents opportunities for developing self-learning and
self-adapting fusion algorithms that can optimize performance under varying scenarios.
4. Open Questions and Future Directions: While significant progress has been made in machine learningbased sensor fusion techniques in the IIoT, several open questions and research directions remain. This
800
study discuss some intriguing open questions that require further investigation:
• Standardization and Interoperability: Developing standardized frameworks and protocols for sensor fusion in IIoT systems is essential to ensure interoperability and seamless integration across
different platforms and devices.
30
• Resource-Constrained Environments: Exploring efficient fusion techniques that can operate in
resource-constrained environments, such as low-power sensors or networks with limited computational capabilities, is an important area for future research.
• Real-time Adaptive Fusion: Investigating real-time adaptive fusion algorithms that can dynamically adjust fusion strategies and parameters based on changing environmental conditions and
system requirements is crucial for achieving optimal performance in
4. Conclusion
ro
of
In this survey paper, we have provided an extensive overview of machine learning-based sensor fusion
techniques in the context of the Industrial Internet of Things (IIoT). The IIoT has emerged as a transformative force in industrial settings, connecting a diverse range of intelligent devices and enabling more efficient
-p
production processes. Sensor fusion plays a crucial role in harnessing the potential of IIoT by integrating
re
data from various sensors, enabling predictive analysis, fault detection, and other critical functions. Our
survey has explored a wide spectrum of sensor fusion techniques, including machine learning models such
lP
as Artificial Neural Networks (ANNs), Back Propagation Neural Networks (BPNN), and Gaussian Mixture
Models (GMM), among others. We have discussed the application of these techniques in fault prediction,
na
adaptive monitoring, and quality control in industrial processes. Furthermore, we have highlighted the challenges and complexities associated with implementing sensor fusion in IIoT systems, emphasizing the need
ur
for robust methodologies and scalable solutions. This study present a set of research directions and trends
in data-driven industrial prediction. As IIoT continues to evolve, addressing these challenges will be crucial
Jo
for realizing its full potential in industrial applications. In conclusion, this survey paper provides valuable
insights into the state-of-the-art sensor fusion techniques in IIoT and their applications. We hope that this
comprehensive overview will serve as a valuable resource for researchers, practitioners, and decision-makers
in the field of industrial automation and contribute to the continued advancement of IIoT technologies.
Future research directions include exploring more advanced fusion algorithms, addressing security and privacy concerns, and developing standards for interoperability to ensure the seamless integration of IIoT into
industrial ecosystems.
References
[1] R. M. Aileni, S. George, S. Pasca, V. S. Carlos Alberto, Data fusion-based ai algorithms in the context of iiots, in:
Internet of Things for Industry 4.0, Springer, 2020, pp. 17–38.
[2] G. Kanagachidambaresan, R. Anand, E. Balasubramanian, V. Mahima, Internet of Things for Industry 4.0: Design,
Challenges and Solutions, Springer, 2020.
[3] M. Huang, Z. Liu, Y. Tao, Mechanical fault diagnosis and prediction in iot based on multi-source sensing data fusion,
Simulation Modelling Practice and Theory 102 (2020) 101981.
31
[4] F. Civerchia, S. Bocchino, C. Salvadori, E. Rossi, L. Maggiani, M. Petracca, Industrial internet of things monitoring
solution for advanced predictive maintenance applications, Journal of Industrial Information Integration 7 (2017) 4–12.
[5] M. E. Korkmaz, M. K. Gupta, Z. Li, G. M. Krolczyk, M. Kuntoğlu, R. Binali, N. Yaşar, D. Y. Pimenov, Indirect
monitoring of machining characteristics via advanced sensor systems: A critical review, The International Journal of
Advanced Manufacturing Technology 120 (11-12) (2022) 7043–7078.
[6] R. Binali, M. Kuntoğlu, D. Y. Pimenov, Ü. A. Usca, M. K. Gupta, M. E. Korkmaz, Advance monitoring of hole machining
operations via intelligent measurement systems: A critical review and future trends, Measurement (2022) 111757.
[7] M. Kuntoğlu, A. Aslan, D. Y. Pimenov, Ü. A. Usca, E. Salur, M. K. Gupta, T. Mikolajczyk, K. Giasin, W. Kaplonek,
S. Sharma, A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical
analysis and trends, Sensors 21 (1) (2020) 108.
[8] D. K. Sah, T. Amgoth, K. Cengiz, Energy efficient medium access control protocol for data collection in wireless sensor
ro
of
network: A q-learning approach, Sustainable Energy Technologies and Assessments 53 (2022) 102530.
[9] A. Diez-Olivan, Data fusion and machine learning for industrial prognosis: Trends and perspectives towards industry
4.0, Information Fusion 50 92–111.
[10] Industrie 4.0 working group, recommendations for implementing the strategic initiative industrie 4.0, Technical report,.
-p
[11] M. Blanchet, T. Rinn, G. Thaden, G., De thieulloy, industry 4.0: The new industrial revolution – how europe will
succeed, roland berger strategy consultants.
re
[12] D. Sah, C. Shivalingagowda, D. P. Kumar, Optimization problems in wireless sensors networks, Soft computing in wireless
sensor networks 2018 (2018) 41–62.
lP
[13] M. Rüßmann, M. Lorenz, P. Gerbert, M. Waldner, J. Justus, P. Engel, M. Harnisch, Industry 4.0: The Future of
Productivity and Growth in Manufacturing Industries, Boston Consulting Group.
[14] Y. Lu, Industry 4.0: A survey on technologies, Applications and Open Research Issues, J. Ind. Inf. Integr 6 1–10.
na
[15] S. Jeschke, C. Brecher, T. Meisen, D. Özdemir, T. Eschert, Industrial internet of things and cyber manufacturing systems,
in: Industrial Internet of Things, Springer, p. 3–19.
ur
[16] V. Roblek, M. Meško, A. Krapež, A complex view of industry 4.0, SAGE Open 6 (2) 1–11.
[17] H. Song, D. Rawat, S. Jeschke, C. Brecher, Cyber-physical systems: Foundations, principles and applications, Academic
Jo
Press, London.
[18] H. Kagermann, J. Helbig, A. Hellinger, W. Wahlster, Recommendations for implementing the strategic initiative industrie
4.0: Securing the future of german manufacturing industry, Final report of the industrie 4.0, Working Group.
[19] L. Snidaro, J. Garcı́a, J. Llinas, Context-based information fusion: a survey and discussion, Information Fusion 25 (2015)
16–31.
[20] D. K. Sah, T. Amgoth, Parametric survey on cross-layer designs for wireless sensor networks, Computer Science Review
27 (2018) 112–134.
[21] X. Zhang, K. P. Rane, I. Kakaravada, M. Shabaz, Research on vibration monitoring and fault diagnosis of rotating
machinery based on internet of things technology, Nonlinear Engineering 10 (1) (2021) 245–254.
[22] V. Saranya, M. Carmel Mary Belinda, G. Kanagachidambaresan, An evolution of innovations protocols and recent
technology in industrial iot, in: Internet of Things for Industry 4.0, Springer, 2020, pp. 161–175.
[23] F. Chen, Z. Fu, Z. Yang, Wind power generation fault diagnosis based on deep learning model in internet of things (iot)
with clusters, Cluster Computing 22 (6) (2019) 14013–14025.
[24] H. Cherif, A. Benakcha, I. Laib, S. E. Chehaidia, A. Menacer, B. Soudan, A. Olabi, Early detection and localization of
stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor, Energy 212 (2020)
118684.
[25] M. Kuntoğlu, E. Salur, M. K. Gupta, M. Sarıkaya, D. Y. Pimenov, A state-of-the-art review on sensors and signal
32
processing systems in mechanical machining processes, The International Journal of Advanced Manufacturing Technology
116 (2021) 2711–2735.
[26] D. Y. Pimenov, A. Bustillo, S. Wojciechowski, V. S. Sharma, M. K. Gupta, M. Kuntoğlu, Artificial intelligence systems
for tool condition monitoring in machining: Analysis and critical review, Journal of Intelligent Manufacturing 34 (5)
(2023) 2079–2121.
[27] M. Kuntoğlu, H. Sağlam, Investigation of signal behaviors for sensor fusion with tool condition monitoring system in
turning, Measurement 173 (2021) 108582.
[28] Y. Djenouri, A. Belhadi, G. Srivastava, E. H. Houssein, J. C.-W. Lin, Sensor data fusion for the industrial artificial
intelligence of things, Expert Systems 39 (5) (2022) e12875.
[29] D. K. Sah, T. N. Nguyen, M. Kandulna, K. Cengiz, T. Amgoth, 3d localization and error minimization in underwater
sensor networks, ACM Transactions on Sensor Networks (TOSN) (2022).
ro
of
[30] X. Ma, P. Wang, B. Zhang, M. Sun, An improved multi-channels information fusion model with multi-scale signals for
fault diagnosis, in: 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things
Engineering (ICBAIE), IEEE, 2021, pp. 552–555.
[31] P. Fu, J. Wang, X. Zhang, L. Zhang, R. X. Gao, Dynamic routing-based multimodal neural network for multi-sensory
-p
fault diagnosis of induction motor, Journal of Manufacturing Systems 55 (2020) 264–272.
[32] H. Shao, J. Lin, L. Zhang, D. Galar, U. Kumar, A novel approach of multisensory fusion to collaborative fault diagnosis
re
in maintenance, Information Fusion 74 (2021) 65–76.
[33] F. Yang, Q. Wu, X. Hu, J. Ye, Y. Yang, H. Rao, R. Ma, B. Hu, Internet-of-things-enabled data fusion method for sleep
lP
healthcare applications, IEEE Internet of Things Journal 8 (21) (2021) 15892–15905.
[34] Y. Chi, Y. Dong, J. Wang, F. R. Yu, V. C. Leung, Knowledge-based fault diagnosis in industrial internet of things: A
survey, IEEE Internet of Things Journal (2022).
na
[35] X. Ren, C. Li, X. Ma, F. Chen, H. Wang, A. Sharma, G. S. Gaba, M. Masud, Design of multi-information fusion based
intelligent electrical fire detection system for green buildings, Sustainability 13 (6) (2021) 3405.
ur
[36] A. Diez-Olivan, J. Del Ser, D. Galar, B. Sierra, Data fusion and machine learning for industrial prognosis: Trends and
perspectives towards industry 4.0, Information Fusion 50 (2019) 92–111.
Jo
[37] Ö. Gültekin, E. Cinar, K. Özkan, A. Yazıcı, Multisensory data fusion-based deep learning approach for fault diagnosis
of an industrial autonomous transfer vehicle, Expert Systems with Applications 200 (2022) 117055.
[38] H. Shao, H. Jiang, F. Wang, H. Zhao, An enhancement deep feature fusion method for rotating machinery fault diagnosis,
Knowledge-Based Systems 119 (2017) 200–220.
[39] P. Joshi, A. S. Raghuvanshi, A multi-objective metaheuristic approach based adaptive clustering and path selection in iot
enabled wireless sensor networks, International Journal of Computer Networks and Applications 8 (5) (2021) 566–584.
[40] Z. Xu, M. Bashir, W. Zhang, Y. Yang, X. Wang, C. Li, An intelligent fault diagnosis for machine maintenance using
weighted soft-voting rule based multi-attention module with multi-scale information fusion, Information Fusion 86 (2022)
17–29.
[41] X. Ding, Q. He, N. Luo, A fusion feature and its improvement based on locality preserving projections for rolling element
bearing fault classification, Journal of Sound and Vibration 335 (2015) 367–383.
[42] P. Joshi, A. S. Raghuvanshi, Hybrid approaches to address various challenges in wireless sensor network for iot applications: opportunities and open problems, International Journal of Computer Networks and Applications 8 (3) (2021)
151–187.
[43] M. A. Munnaf, G. Haesaert, M. Van Meirvenne, A. Mouazen, Site-specific seeding using multi-sensor and data fusion
techniques: A review, Advances in agronomy 161 (2020) 241–323.
[44] P. Joshi, S. Gavel, A. S. Raghuvanshi, Estimating energy consumption for various sensor node distributions in wireless
33
sensor networks, in: Nanoelectronics, Circuits and Communication Systems: Proceeding of NCCS 2019, Springer, 2021,
pp. 289–299.
[45] W. Ding, X. Jing, Z. Yan, L. T. Yang, A survey on data fusion in internet of things: Towards secure and privacy-preserving
fusion, Information Fusion 51 (2019) 129–144.
[46] T. Meng, X. Jing, Z. Yan, W. Pedrycz, A survey on machine learning for data fusion, Information Fusion 57 (2020)
115–129.
[47] M. Younan, E. H. Houssein, M. Elhoseny, A. A. Ali, Challenges and recommended technologies for the industrial internet
of things: A comprehensive review, Measurement 151 (2020) 107198.
[48] A. Krishnakumari, A. Elayaperumal, M. Saravanan, C. Arvindan, Fault diagnostics of spur gear using decision tree and
fuzzy classifier, Int. J. Adv. Manuf (nol.89 (9-12) 3487–3494.
[49] M. Ruiz, L. Mujica, S. Alférez, L. Acho, C. Tutivén, Y. Vidal, J. Rodellar, F., Pozo,wind turbine fault detection and
ro
of
classification by means of image texture analysis,, Mech Syst. Signal Process 107 149–167.
[50] C. Shivalingagowda, H. Ahmad, P. Jayasree, D. K. Sah, Wireless sensor network routing protocols using machine learning,
Architectural Wireless Networks Solutions and Security Issues (2021) 99–120.
[51] A. Yunusa-Kaltungo, J. Sinha, Effective vibration-based condition monitoring (evcm) of rotating machines, J. Qual.
-p
Maint. Eng 23-3 279–296.
[52] A. Ragab, M. El-Koujok, B. Poulin, M. Amazouz, S. Yacout, Fault diagnosis in industrial chemical processes using
re
interpretable patterns based on logical analysis of data, Expert Syst. Appl 95 368–383.
[53] G. Manco, E. Ritacco, P. Rullo, L. Gallucci, W. Astill, D. Kimber, M. Antonelli, Fault detection and explanation through
lP
big data analysis on sensor streams, Expert Syst. Appl 87 141–156.
[54] A. Diez-Olivan, M. Penalva, F. Veiga, L. Deitert, R. Sanz, B. Sierra, C. Li, R.-V. Sanchez, G. Zurita, M. Cerrada, D. Cabrera, R. Vásquez, Kernel density-based pattern classification in blind fasteners installation, International Conference on
na
Hybrid Artificial Intelligence Systems 76 195–206 20.
[55] J. Yin, W. Zhao, Fault diagnosis network design for vehicle on-board equipments of high-speed railway: a deep learning
ur
approach,, Eng Appl. Artif. Intell 56.
[56] C. Liu, Y. Li, G. Zhou, W. Shen, A sensor fusion and support vector machine based approach for recognition of complex
Jo
machining conditions, J. Intell. Manuf.
[57] A. Diez-Olivan, X. Averós, R. Sanz, B. Sierra, I. Estevez, Quantile regression forests-based modeling and environmental
indicators for decision support in broiler farming,, Comput Electron. AgricIn press.
[58] E. Peterson, S. Cunningham, M. Thomas, S. Collings, G. Bonnett, B. Harch, An assessment framework for measuring
agro-ecosystem health, Ecol. Indic 79.
[59] D. Sun, V. Lee, Y. Lu, An intelligent data fusion framework for structural health monitoring, in: 11th IEEE Conference
on Industrial Electronics and Applications (ICIEA, p. 49–54.
[60] V. Jaramillo, J. Ottewill, R. Dudek, D. Lepiarczyk, P. Pawlik, Condition monitoring of distributed systems using twostage bayesian inference data fusion,, Mech Syst. Signal Process 87 91–110.
[61] A. Diez, N. Khoa, M. Alamdari, Y. Wang, F. Chen, P. Runcie, A clustering approach for structural health monitoring
on bridges, J. Civil Struct. Health Monitoring 6 (3) 429–445.
[62] O. Basurko, Z. Uriondo, A. Diez-Olivan, J. Pagan, R. Sanz, B. Sierra, Condition-based maintenance for medium speed
diesel engines used in vessels in operation,, Appl Therm. Eng 80 404–412, 34.
[63] G. Niu, H. Li, Ietm centered intelligent maintenance system integrating fuzzy semantic inference and data fusion, microelectron, Reliab 75 197–204.
[64] F. Kadri, F. Harrou, S. Chaabane, Y. Sun, C. Tahon, Seasonal arma-based spc charts for anomaly detection: application
to emergency department systems, Neurocomputing 173 2102–2114.
34
[65] A. Kumar, R. Shankar, L. Thakur, A big data driven sustainable manufacturing framework for condition-based maintenance prediction, J. Comput. SciInpress.
[66] H. Li, H.-Z. Huang, Y.-F. Li, J. Zhou, J. Mi, Physics of failure based reliability prediction of turbine blades using
multi-source information fusion, Appl. Soft ComputIn press.
[67] I. Animah, M. Shafiee, Condition assessment, remaining useful life prediction and life extension decision making for
offshore oil and gas assets, J. Loss Prev. Process Ind 53 17–28.
[68] J. Wu, Y. Su, Y. Cheng, X. Shao, C. Deng, C. Liu, Multi-sensor information fusion for remaining useful life prediction
of machining tools by adaptive network based fuzzy inference system, Appl. Soft Comput 68 13–23.
[69] G. Niu, J. Jiang, Prognostic control-enhanced maintenance optimization for multi- -component systems,, Reliab Eng.
Syst. Saf 168 218–226.
[70] A. Ragab, M.-S. Ouali, S. Yacout, H. Osman, Remaining useful life prediction using prognostic methodology based on
ro
of
logical analysis of data and kaplan–meier estimation, J. Intell. Manuf 27 (5) 943–958.
[71] L. Cristaldi, G. Leone, R. Ottoboni, S. Subbiah, S. Turrin, A comparative study on data-driven prognostic approaches
using fleet knowledge, in: IEEE International Conference on Instrumentation and Measurement Technology (I2MTC, p.
1–6.
-p
[72] H. Shi, J. Zeng, Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for
multi-component systems considering stochastic dependence, Comput. Ind. Eng 93 (Suppl C) 192–204.
re
[73] A. Kumar, R. Chinnam, F. Tseng, An hmm and polynomial regression based approach for remaining useful life and
health state estimation of cutting tools, Comput. Ind. EngIn press.
lP
[74] X. Zhou, K. Huang, L. Xi, J. Lee, Preventive maintenance modeling for multi-component systems with considering
stochastic failures and disassembly sequence, reliab, Eng. Syst. Saf 142 (Suppl C) 231–237.
[75] Z. Li, J. Guo, R. Zhou, Maintenance scheduling optimization based on reliability and prognostics information, in: Annual
na
Reliability and Maintainability Symposium (RAMS, p. 1–5.
[76] Y. Zeng, A. Che, X. Wu, Bi-objective scheduling on uniform parallel machines considering electricity cost, eng, Optim
ur
50 (1) 19–36.
[77] C. Garcia-Santiago, J. Ser, C. Upton, F. Quilligan, S. Gil-Lopez, S. Salcedo-Sanz, A random-key encoded harmony search
Jo
approach for energy-efficient production scheduling with shared resources, eng, Optim 47 (11) 1481–1496.
[78] M. Gen, W. Zhang, L. Lin, Y. Yun, Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing
scheduling, Comput. Ind. Eng 112 616–633.
[79] I. Moon, S. Lee, M. Shin, K. Ryu, Evolutionary resource assignment for workload–based production scheduling, J. Intell.
Manuf 27 (2) 375–388.
[80] X. Fang, N. Gebraeel, K. Paynabar, Scalable prognostic models for large-scale condition monitoring applications, IISE
Trans 49-7 698–710.
[81] C. Bierwirth, D. Mattfeld, Production scheduling and rescheduling with genetic algorithms,, Evol Comput 7 (1) 1–17.
1000
[82] H. Wang, W. Wang, H. Sun, Z. Cui, S. Rahnamayan, S. Zeng, A new cuckoo search algorithm with hybrid strategies for
flow shop scheduling problems, Soft Comput 21 (15) 4297–4307.
[83] M. Zandieh, A. Khatami, S. Rahmati, Flexible job shop scheduling under condition-based maintenance: improved version
of imperialist competitive algorithm, Appl. Soft Comput 58 449–464.
[84] J. Costello, G. West, S. McArthur, Machine learning model for event-based prognostics in gas circulator condition
monitoring, IEEE Trans. Reliab 66 (4) 1048–1057.
[85] Y. Fu, J. Ding, H. Wang, J. Wang, Two-objective stochastic flow-shop scheduling with deteriorating and learning effect
in industry 4.0-based manufacturing system, Appl. Soft Comput 68 847–855.
[86] F. Khoukhi, J. Boukachour, A. Alaoui, The dual-ants colony: a novel hybrid approach for the flexible job shop scheduling
35
problem with preventive maintenance, Comput. Ind. Eng 106 236–255.
[87] C. Li, R.-V. Sánchez, G. Zurita, M. Cerrada, D. Cabrera, Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning, Sensors 16 (6) 895.
[88] M. Khatami, S. Zegordi, Coordinative production and maintenance scheduling problem with flexible maintenance time
intervals, J. Intell. Manuf 28 (4) 857–867.
[89] M. Ventresca, B. Ombuki, Ant colony optimization for job shop scheduling problem, in: 8th IASTED International
Conference on Artificial Intelligence and Soft Computing, p. 451–152.
[90] R.-H. Huang, T.-H. Yu, An effective ant colony optimization algorithm for multi–objective job-shop scheduling with
equal-size lot-splitting, Appl. Soft Comput 57 642–656.
[91] A. Ninawe, A. K. Mallick, V. Yadav, H. Ahmad, D. K. Sah, C. Barna, Cathedral and indian mughal monument recognition
using tensorflow, in: Soft Computing Applications: Proceedings of the 8th International Workshop Soft Computing
ro
of
Applications (SOFA 2018), Vol. I 8, Springer, 2021, pp. 186–196.
[92] W. Liao, M. Chen, X. Yang, Joint optimization of preventive maintenance and production scheduling for parallel machines
system, J. Intell. Fuzzy Syst 32 (1) 913–923.
maintenance planning, J. Manuf. Syst 37 182–192.
-p
[93] S. Wang, M. Liu, Multi-objective optimization of parallel machine scheduling integrated with multi-resources preventive
[94] D. Sha, H.-H. Lin, A multi-objective pso for job-shop scheduling problems,, Expert Syst Appl 37 (2) 1065–1070.
re
[95] W. Liao, X. Zhang, M. Jiang, Multi-objective group scheduling optimization integrated with preventive maintenance,,
Eng Optim 1–15.
lP
[96] H. Seidgar, M. Zandieh, I. Mahdavi, An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop
problem with preventive maintenance activities and reliability approach, Int. J. Ind. Syst. Eng 26 (1) 16–41.
[97] D. Gupta, C. Maravelias, J. Wassick, From rescheduling to online scheduling, Chem. Eng. Res. Des 116 83–97.
na
[98] A. Mosallam, K. Medjaher, N. Zerhouni, Data-driven prognostic method based on bayesian approaches for direct remaining useful life prediction, J. Intell. Manuf 27 (5) 1037–1048.
ur
[99] J. Lee, C. Jin, Z. Liu, H. Ardakani, Introduction to data-driven methodologies for prognostics and health management,
in: Probabilistic Prognostics and Health Management of Energy Systems, Springer, p. 9–32.
Jo
[100] M. Safizadeh, S. Latifi, Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell,, Inf Fusion 18 1–8.
[101] L. Guo, N. Li, F. Jia, Y. Lei, J. Lin, A recurrent neural network based health indicator for remaining useful life prediction
of bearings, Neurocomputing 240 98–109.
[102] Y. Zhang, S. Ren, Y. Liu, T. Sakao, D. Huisingh, A framework for big data driven product lifecycle management, J.
Cleaner Prod 159 229–240.
[103] S. Spielberg, R. Gopaluni, P. Loewen, Deep reinforcement learning approaches for process control, in: 6th IEEE International Symposium on Advanced Control of Industrial Processes (AdCONIP, p. 201–206.
[104] X. Li, Q. Ding, J.-Q. Sun, Remaining useful life estimation in prognostics using deep convolution neural networks,, Reliab
Eng. Syst. Saf 172 1–11.
[105] G. Shroff, P. Agarwal, K. Singh, A. Kazmi, S. Shah, A. Sardeshmukh, Prescriptive information fusion, in: 17th IEEE
International Conference on Information Fusion (FUSION, p. 1–8.
[106] Z. Hong, Y. Feng, Z. Li, G. Tian, J. Tan, Reliability-based and cost-oriented product optimization integrating fuzzy
reasoning petri nets, interval expert evaluation and cultural-based dmopso using crowding distance sorting, Appl. Sci
7 (8) 791.
[107] G. Gu, C.-T. Chen, M. Buehler, De novo composite design based on machine learning algorithm, Extreme Mech. Lett
18 19–28.
36
[108] S. Kang, E. Kim, J. Shim, S. Cho, W. Chang, J. Kim, Mining the relationship between production and customer service
data for failure analysis of industrial products, Comput. Ind. Eng 106 137–146.
[109] T. Shiraiwa, F. Briffod, Y. Miyazawa, M. Enoki, Fatigue performance prediction of structural materials by multi-scale
modeling and machine learning, in: 4th World Congress on Integrated Computational Materials Engineering (ICME
2017, Springer, p. 317–326.
[110] X. Gao, Y. Chen, D. You, Z. Xiao, X. Chen, Detection of micro gap weld joint by using magneto-optical imaging and
kalman filtering compensated with rbf neural network,, Mech Syst. Signal Process 84 570–583.
[111] A. Diez-Olivan, J. Pagan, N. Khoa, R. Sanz, B. Sierra, Kernel-based support vector machines for automated health
status assessment in monitoring sensor data, Int. J. Adv. Manuf. Technol 95 327–340.
[112] B. Al-Najjar, I. Alsyouf, Selecting the most efficient maintenance approach using fuzzy multiple criteria decision making.
[113] M. Sakawa, R. Kubota, Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy
ro
of
duedate through genetic algorithms, Eur. J. Oper. Res 120 (2) 393–407.
[114] M. Biondi, G. Sand, I. Harjunkoski, Optimization of multipurpose process plant operations: a multi-time-scale maintenance and production scheduling approach, Comput. Chem. Eng 99 325–339.
problem in flow shops, Comput. Ind. Eng 115 342–353.
-p
[115] W. Cui, Z. Lu, C. Li, X. Han, A proactive approach to solve integrated production scheduling and maintenance planning
[116] M. Mazhar, S. Kara, H. Kaebernick, Remaining life estimation of used components in consumer products: Life cycle
re
data analysis by weibull and artificial neural networks, J. Oper. Manage 25 (6) 1184–1193.
[117] M. Klein, Calculating life cycle impact assessment of chemicals with neural networks, Chemie Ingenieur Technik 86 (9)
lP
1631–1631.
[118] A. Boudjelida, On the robustness of joint production and maintenance scheduling in presence of uncertainties, J. Intell.
Manuf 1–16.
62 (5) 1646–1667.
na
[119] N. Lappas, C. Gounaris, Multi-stage adjustable robust optimization for process scheduling under uncertainty, AIChE J
ur
[120] O. Arık, M. Toksarı, Multi-objective fuzzy parallel machine scheduling problems under fuzzy job deterioration and
learning effects, Int. J. Prod. Res 1–18.
Jo
[121] C. Baban, M. Baban, M. Suteu, Using a fuzzy logic approach for the predictive maintenance of textile machines, J. Intell.
Fuzzy Syst 30 (2) 999–1006.
[122] F. Wang, S. Pan, Y. Xiong, H. Fang, D. Wang, Research on software architecture of prognostics and health management
system for civil aircraft, in: International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC, p.
510–513.
37
CONFLICT OF INTEREST
Date: 29.12.2022
Attention:
ManagingEditor
journal - Measurement: Sensors – Elsevier
2665-9174
Dear editor/manager,
In conjunction with the submission of the unpublished original manuscript Titled
-p
ro
o
f
“Paper Title: A survey on machine learning based sensor fusion techniques in Industrial Internet
of Things “. sent via the online system of Evise for your consideration, peer-review and
possible publication in the journal - Measurement: Sensors – Elsevier ISSN: 2665-9174
lP
Sincerely
re
I clearly state that I have the consent from all contributing authors to submit the manuscript
and all of them accept complete responsibility for its contents. None of the authors declare any
conflicts of interest.
List of all contributing authors (full names in regular order):
Jo
ur
na
Dr Deepak sharmaa, Anuj kumarb, Dr. Nitin Tyagic, Dr. Sunil S. Chavand, Syam
Machinathu Parambil Gangadharane
“I hereby confirm and electronically sign this agreement form on behalf of all above listed
contributing authors.”
Authors Signature
Dr. Sunil S. Chavan
Smt.Indira Gandhi College of engineering.Navi Mumbai.
Affiliation: University of Mumbai
Email id: chavan_s_s@hotmail.com
Orcid id:0000-0002-5913-6960