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Applied Acoustics
Volume 195, 30 June 2022, 108839
An efficient diagnosis approach for bearing faults
using sound quality metrics
Author links open overlay
panelTauheed Mian , Anurag Choudhary , Shahab Fatima
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Abstract
The perception of sound quality is an important source of
information and can be used as a promising indicator to diagnose
various faults in rotating machines. This work presented a
methodology involving the detection of bearing faults using sound
quality metrics. Head and Torso Simulator (HATS) was used to
acquire the sound signals of different bearing fault conditions with a
minimal background noise using a semi-anechoic chamber. After
that, various sound quality features were extracted from the
acquired signals, and descriptive analysis for five bearing conditions
was presented based on these features. Moreover, these features
were used to develop a self-adaptive fault diagnosis system using a
Support Vector Machine (SVM). Experimental validation of the
proposed method was also done with different background
conditions and with different microphones. The results showed that
the proposed methodology is reliable and effective for accurate fault
diagnosis of bearings in a non-invasive manner.
Introduction
Bearing is one of the most critical components of rotating machines,
and the diagnosis of this component is essential for the successful
operation of these machines and effective production [1], [2], [3],
[4]. The timely diagnosis of faults in these components is critical as
it could lead to an increment in fault severity that could further
cause catastrophic machinery failure and significant economic loss
[5], [6], [7]. There are two approaches widely implemented for fault
identification in various rotating machines: the physics-based
approach and the data-driven approach [8], [9]. Physics-based
approaches require the internal structure information of the
components, but data-driven methods do not require any physical
structure information. Data-driven strategies only needed the
historical data of machine operations. The sensor is essential for
validation or data acquisition in a data-driven approach. There are
various sensor-based methods for the diagnosis of faults in these
machines, including vibration monitoring [10], [11], thermal
imaging [12], [13], noise monitoring [14], acoustic emission [15],
current monitoring [16], and a combination of these methods [17],
[18]. Among these diagnosis techniques, vibration-based
monitoring has been in the application for a long time and is the
most established method. However, in most cases, it is used
through contact methods. Other methods also have limitations,
such as thermal imaging largely depending on temperature change,
which is not a frequent condition in actual practice. In recent
decades, acoustic or sound-based monitoring has been getting much
attention from researchers and condition monitoring engineers.
This method can be implemented to identify faults in rotating
machines in a non-invasive manner as the sound emitted by various
fault conditions behaves differently, which could be used as the
leading indicator of fault identification.
The traditional methodology of fault diagnosis using acoustic signal
usually consists of extracting statistical features from the signal in
the time domain and frequency domain or time–frequency domain
[19], [20], [21]. Statistical features only focus on the quantitative
analysis of sound emitted from various components of rotating
machines. However, the sound has another important aspect called
sound quality. There are various sound quality features or
psychoacoustic features (known as sound quality metrics) that can
be used in quantitative and qualitative ways. Sound quality can
easily depict how irritating the sound is for a human, and it is a
well-known aspect that humans are the most important and
integrated part of any industrial system. The quantity and quality of
production depend on the efficiency of this workforce [22]. Thus,
the sound quality parameters alone or with time–frequency analysis
can be used for acoustic analysis in quantitative and qualitative
ways [23]. The sound quality features are more related to the
perception of sound by an adult human ear and have much
potential to diagnose faults in rotating machines as against other
methods [24]. The human ear's sensitivity is most to the lower part
of the audible frequency range (20 Hz - 20000 Hz), and the
perception of sound pressure becomes much lower on high
frequencies in this range [25]. The sound emitted due to different
faults could be annoying to workers and lead to asset loss and
workforce productivity. So, it is good to focus on the amount of
generated sound and its quality. In industries, the workforce
remains working for long hours, and the harshness of sound
generated due to the various faults in the rotating components may
affect their working efficiency.
Researchers have used traditional time, frequency, and time–
frequency domain information from the sound signals to diagnose
faults using various machine learning and deep learning-based
methodologies. A few types of research were published on sound
quality, but less is reported on condition monitoring and fault
diagnosis of rotating machine components. More work was focused
on statistical feature extraction-based fault diagnosis like a bearing
fault detection method reported with an acoustic signal and
decision tree-based feature extraction. Finally, the Bayes classifier
discriminates the different faulty classes of bearing [26]. A novel
feature extraction method, MSAF-RATIO30-MULTIEXPANDED,
extracted features from the frequency spectrum of the data obtained
from a single-phase induction motor. Three algorithms of machine
learning k-Nearest Neighbour (k-NN), k-means clustering, and
linear perception were explored to classify faults with a promising
range of accuracies [27]. Analytical wavelet transform was used to
convert the acoustic data from the microphone to the images, and a
Convolutional Neural Network (CNN) was used to classify faults in
a centrifugal pump with bearing and impeller faults, and CNN
outperformed the traditional machine learning methods [28].
Traditional methods based on time or frequency domain features
provide only statistical average and have limitations against
transient and non-stationary signals. On the other hand, the
calculation of sound quality parameters involves simultaneous
analysis of time and frequency and hence is more sensitive towards
the transient in the signals. Unlike the traditional statistical
features, they have no limitations on temporal or frequency
resolution [29], [30]. So, proper analysis of sound quality
parameters will benefit in the two ways, i.e., in terms of accurate
fault diagnosis of machinery and the workforce's consideration,
which is one of the significant advantages of sound quality
parameters against the statistical time or frequency domain
features. Sound quality can be analyzed through various prevalent
sound quality or psychoacoustic features, including loudness,
tonality, sharpness, roughness, articulation index, tone to noise
ratio, etc. [25], [31]. These parameters are utilized to evaluate the
subjective perception of human beings [32]. The sound quality of a
sound largely influences the psychological and physiological
perception of humans. Sound quality is prevalent in automotive
applications for interior and exterior noise analysis [33]. Also, the
sound quality analysis is helpful for the analysis of noise radiated
from the engine [34]. This can also be used for other mechanical
components, such as sound quality-based evaluation of noise
generated from axial piston pump was done using an Artificial
Neural Network (ANN) [35]. Research using five-sound quality
parameters and one traditional time-domain feature was reported
to detect engine misfiring using a microphone, and 94 %
classification accuracy was achieved with SVM [29]. Researchers
have recently focused on using psychoacoustic theory for fault
diagnosis in engineering applications [36]. Cavitation detection in
centrifugal pumps was done using the psychoacoustic approach
with various signal descriptors. A total of three cavitation conditions
were considered the absence of cavitation, onset of cavitation, and
fully developed cavitation [37]. The vibration, acoustic, and
psychoacoustic features were used for the quality assurance of the
gearbox at the assembly line using an ANN and discriminant
classifier with an accuracy of 93.02 % and 95.93 %, respectively
[24], and binary classification of gear faults as healthy and faulty
using psychoacoustic and statistical features were done [38].
Moreover, the application of psychoacoustic and statistical features
with vibration data for anomaly detection in gearmotors was
presented [39]. However, such correlations need further analysis as
the psychoacoustic features do not have a direct and theoretically
established relationship with the vibration data.
To the best of the authors' knowledge, no work was reported using
the sound quality features with replication of human hearing to
diagnose bearing faults in a rotating machine. Sound quality or
psychoacoustic features have the potential for identifying faults and
need to be explored in detail for the fault diagnosis in bearings. So,
in the present work, an efficient approach for the fault diagnosis of
bearings based on the sound quality features was given with the
replication of human acoustic perception by acquiring the sound
signals using a HATS. The descriptive analysis of various sound
quality features in different fault conditions was presented. Further,
SVM was used to develop a self-adaptive fault diagnosis approach
using these sound quality features. The remaining paper has the
following sections: Section 2 explains the experimentation and data
acquisition, Section 3 describes the adopted methodology, Section 4
explains the results, and the conclusion of the work is given in
Section 5.
Section snippets
Experimentation for data acquisition
The experiments were conducted on a Machine Fault Simulator
(MFS) to simulate various bearing fault conditions. The complete
arrangement of the experimental laboratory setup is shown in Fig.
1. In MFS, a three-phase induction motor of ½ HP was used as the
prime mover, and a variable frequency drive was used to control the
speed of the induction motor. The MFS has provision for mounting
two bearings in aluminum horizontally split bracket housing at
Drive End (DE) and Non-Drive End (NDE). As
Proposed methodology for bearing fault diagnosis
This section elaborates the sound quality-based fault diagnosis
methodology. The present work used six sound quality features:
time-varying loudness, loudness level, fluctuation strength,
roughness, sharpness, and articulation index. The flow diagram of
the adopted methodology for the present work is shown in Fig. 3.
Experimental results and discussion
In this section sound, quality features for various fault conditions
were discussed and analyzed descriptively. The adopted sound
quality features were further used for classification using SVM. The
results were analyzed with 2D confusion matrix by providing the
information of correctly predicted classes against the actual classes.
Also, the class-wise performance was evaluated using different
parameters. The results were also analyzed in different noisy
backgrounds and with different
Conclusion and future scope
A reliable and non-invasive diagnosis approach using sound quality
metrics for bearing faults was proposed in this work. For this
purpose, six prevalent sound quality features were extracted from
sound signals acquired in minimal background noise conditions
using HATS. Descriptive analysis of these features in different fault
conditions was presented. Moreover, to develop a sound qualitybased self-adaptive system, these features were used for the
classification using SVM. The results in the
CRediT authorship contribution statement
Tauheed Mian: Conceptualization, Methodology, Validation,
Writing – original draft. Anurag Choudhary: Investigation,
Visualization, Writing – review & editing. Shahab
Fatima: Supervision, Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to
influence the work reported in this paper.
Acknowledgment
The authors would like to acknowledge the support of 'Ministry of
Education, Goverment of India' for this work.
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