An Adaptive Multi-Sensor Data Fusion Method Based

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sensors
Article
An Adaptive Multi-Sensor Data Fusion Method
Based on Deep Convolutional Neural Networks for
Fault Diagnosis of Planetary Gearbox
Luyang Jing 1 , Taiyong Wang 1, *, Ming Zhao 2 and Peng Wang 1
1
2
*
School of Mechanical Engineering, Tianjin University, Tianjin 300354, China;
jingl[email protected] (L.J.); [email protected] (P.W.)
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
[email protected]
Correspondence: [email protected]; Tel.: +86-22-8740-2033
Academic Editor: Vittorio M. N. Passaro
Received: 30 December 2016; Accepted: 16 February 2017; Published: 21 February 2017
Abstract: A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal
with complicated damage detection problems of mechanical systems. Nevertheless, this approach
suffers from two challenges, which are (1) the feature extraction from various types of sensory data
and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or
fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are
also highly required during these selections. To address these two challenges, we propose an adaptive
multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault
diagnosis. The proposed method can learn features from raw data and optimize a combination of
different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed
method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion
levels, single sensory data, and two traditional intelligent models, back-propagation neural networks
(BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results
demonstrate that the proposed method is able to detect the conditions of the planetary gearbox
effectively with the best diagnosis accuracy among all comparative methods in the experiment.
Keywords: fault diagnosis; multi-sensor data fusion; deep convolutional neural networks;
feature learning; planetary gearbox
1. Introduction
The planetary gearbox is a key component in mechanical transmission systems and has been
widely used in wind turbines, helicopters and other heavy machineries [1]. The wide range of gear
ratios, small room in power transmission line, and high transmission efficiency are the most significant
advantages of planetary gearboxes [2]. Planetary gearboxes generally operate in tough working
environments, which makes them suffer from a variety of failures and damages [1,3] causing unwanted
downtime, economic losses, and even human casualties [4]. Therefore, the fault diagnosis of planetary
gearboxes is necessary to guarantee a safe and efficient operation of mechanical transmission systems.
Health conditions of planetary gearboxes can be reflected by various kinds of measurements,
including vibration signal, acoustic signal, driven motor current, shaft speed, oil debris, etc.
Different measurements have different drawbacks and are sensitive to different types of damage
modes or operation conditions. Thus, combining and analyzing these measurements together should
be an appropriate approach to detect various types of faults of complex systems. This approach,
Sensors 2017, 17, 414; doi:10.3390/s17020414
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named multi-sensor data fusion, can achieve more accurate and reliable result than approaches with
a single sensor [5–7].
Nevertheless, multi-sensor data fusion for fault diagnosis suffers from two challenging
problems [6]. (1) One is the feature extraction of multi-sensory data. Generally, conventional fault
diagnosis includes three steps [8]: signal acquisition, feature extraction, and fault classification.
In the feature extraction step, fault sensitive features are extracted and selected from raw data
through signal processing technologies and data analysis strategies, such as Fourier spectral analysis,
wavelet transformation (WT), and principal component analysis (PCA). However, the multiple types of
sensory data of multi-sensor data fusion may cause a number of issues that make the feature extraction
with multi-sensor much more difficult than with a single sensor. These issues [6,7] include more
imprecision and uncertainties in measurements, various noises, more conflicting or correlating data,
higher data dimensions, etc. Extracting features from these kinds of data will be a very challenging
and cruel task. At the same time, multiple types of sensory data also increases the difficulties and
consumes much time to choose an optimal handcraft feature or manual feature extraction method,
even though to date, the optimal handcraft feature or feature extraction method for a specific type of
sensory data still remains unanswered [8,9]; (2) The other challenge of the multi-sensor data fusion is
the selection of different fusion levels. Similarly, different fusion levels have their own advantages
and disadvantages, and the suitable one for different fault diagnosis tasks is usually different [5].
Selecting an optimal fusion level for a specific fault diagnosis task always requires domain expertise,
prior knowledge, and human labor.
Deep neural networks (DNN), also known as deep learning, have been attracting increasing
attention from researchers from various fields in recent years [10–12]. The key advantage of DNN
is the feature learning ability [11], which can automatically discover an intricate structure and learn
useful features from raw data layer by layer. A number of studies [11–13] have shown that DNN can
fuse input raw data and extract basic information from it in its lower layers, fuse the basic information
into higher representation information and decisions in its middle layers, and further fuse these
decisions and information in its higher layers to form the final classification result. It can be seen that
DNN itself is a fusion structure [11,13], which fuses the feature extraction, feature selection, data-level
fusion, feature-level fusion, decision-level fusion, and classification into one single learning body.
For this reason, a DNN-based low level fusion, e.g., data-level fusion, can not only learn features from
fused raw data automatically, but also fuse these data, features and decisions adaptively through its
deep-layered structure. DNN might be an ideal model to fuse multi-sensory data and detect faults
of a mechanical system. However, although some applications [14–18] of DNN in feature learning
and fault diagnosis with a single sensor have been found in recent years, no study, to the best of
our knowledge, has investigated the effectiveness of DNN-based feature learning and the adaptive
level fusion method for fault diagnosis. It is attractive and meaningful to investigate this adaptive
fusion method, which can learn features from raw data automatically, and select and combine fusion
levels adaptively.
Deep convolutional neural networks (DCNNs), as one of the main types of DNN models,
have been successfully used in mechanical fault diagnosis with automatic feature learning from single
sensory data [9,14,19,20]. Benefitting from several unique structures, DCNN can achieve better results
with less training time than standard neural networks. Firstly, DCNN has a large set of filter kernels
in convolutional layers, which can capture representative information and patterns from raw data.
Stacking these convolutional layers can further fuse information and build complex patterns; Secondly,
DCNN is an unfully-connected network, where each filter shares the same weights. This structure
can reduce both the training time and complication of the model. In addition, the pooling layer of
DCNN further reduces the revolution of the input data as well as the training time, and improves
the robustness of the extracted patterns (a detailed introduction to the DCNN model is presented
in Section 2). Thus, DCNN should have great potential in processing the multi-sensory data of
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a mechanical system, which usually contains rich information in the raw data and is sensitive to
training time as well as model size.
Aiming to address the two problems of multi-sensor data fusion mentioned above, this paper proposes
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an adaptive data fusion method based on DCNN and applies it to detect the health conditions of a planetary
Aiming from
to address
the two problems
of the
multi-sensor
data
fusion is
mentioned
above,
thisfeatures
paper from
gearbox. Different
conventional
methods,
proposed
method
able to (1)
extract
proposes
an
adaptive
data
fusion
method
based
on
DCNN
and
applies
it
to
detect
the
health
raw data automatically and (2) optimize a combination of different fusion levels adaptively for any specific
conditions of a planetary gearbox. Different from conventional methods, the proposed method is
fault diagnosis
task with less dependence on expert knowledge or human labor.
able to (1) extract features from raw data automatically and (2) optimize a combination of different
Thefusion
rest of
the paper is organized as follows. In Section 2, the typical architecture of DCNN and
levels adaptively for any specific fault diagnosis task with less dependence on expert
an adaptive
training
method
are briefly described. Section 3 illustrates the procedures of the proposed
knowledge or human
labor.
method, theThe
design
of
the
DCNN
model,
and several
comparative
introduced
to further
rest of the paper is organized
as follows.
In Section
2, the typicalmethods
architecture
of DCNN and
an
adaptive
training
method
are
briefly
described.
Section
3
illustrates
the
procedures
of
the
analyze the performance of the proposed method. In Section 4, an experimental system of a planetary
method,
thevalidate
design ofthe
the effectiveness
DCNN model, and
several
comparative
methods
introduced
to
gearboxproposed
test rig is
used to
of the
proposed
method.
Finally,
the conclusions
further analyze the performance of the proposed method. In Section 4, an experimental system of a
are drawn in Section 5.
planetary gearbox test rig is used to validate the effectiveness of the proposed method. Finally, the
conclusions are drawn in Section 5.
2. Deep Convolutional Neural Networks
2. Deep Convolutional Neural Networks
2.1. Architecture of Deep Convolutional Neural Networks
2.1. Architecture of Deep Convolutional Neural Networks
DCNN
is a type of DNN model inspired by visual system structure [11,21], and it has
is a type
of DNNfor
model
inspired
by visual system
[11,21],
andinitimage
has become
become theDCNN
dominant
approach
almost
all recognition
andstructure
detection
tasks
and speech
the
dominant
approach
for
almost
all
recognition
and
detection
tasks
in
image
and
speech
analysis
analysis [22–24]. DCNN contains three kinds of layers [25], which are the convolutional layer,
[22–24]. DCNN contains three kinds of layers [25], which are the convolutional layer, pooling layer,
pooling layer, and fully-connected layer. As shown in Figure 1, the first several layers of a typical
and fully-connected layer. As shown in Figure 1, the first several layers of a typical DCNN usually
DCNN consist
usuallyofconsist
of a combination
of two
types of layers—convolutional
followed by
a combination
of two types
of layers—convolutional
layers, followedlayers,
by pooling
poolinglayers—and
layers—and
is a fully-connected
In the following
part,
we will
thethe
last last
layerlayer
is a fully-connected
layer. Inlayer.
the following
part, we will
describe
thesedescribe
threekinds
kinds of layers
detail.
these three
layersininmore
more
detail.
Convolutional and pooling layer
Input data
Convolutional and pooling layer
subsampling
Convolutional filters
Fully connected layer
subsampling
Feature
maps
Feature
maps
Softmax regression
subsampling
subsampling
Convolution
Convolution
Feature
maps
Fully connect
Feature
maps
subsampling
subsampling
Feature
maps
Feature
maps
Figure 1. A typical architecture of deep convolutional neural network (DCNN).
Figure 1. A typical architecture of deep convolutional neural network (DCNN).
The convolutional layer is composed of a number of two-dimensional (2D) filters with
parameters.
filters convolute
with input
data and obtain an
output,
as
Theweighted
convolutional
layerThese
is composed
of a number
of two-dimensional
(2D)
filtersnamed
with weighted
feature
maps.
Each
filter
shares
the
same
weighted
parameters
for
all
the
patches
of
the
input
data
to maps.
parameters. These filters convolute with input data and obtain an output, named as feature
reduce the training time and complication of the model, which is different from a traditional neural
Each filter shares the same weighted parameters for all the patches of the input data to reduce the
network with different weighted parameters for different patches of the input data. Suppose the
traininginput
timeofand
complication of the model, which is different from a traditional neural network
the convolutional layer is , which belongs to × , and A and B are the dimensions of the
with different
weighted
different patches
the inputasdata.
Suppose
the input of the
input data. Then theparameters
output of thefor
convolutional
layer can of
be calculated
follows
[26]:
A
×
B
convolutional layer is X, which belongs to R  , and A and B are the dimensions of the input data.
−1

 follows [26]:
Then the output of the convolutional layer
be calculated
as
(1)
 = can
(∑
 ∗  + 
 )
=1
CC
Ccn = f
∑
cc=1
!
l −1
Xcc
l
∗ Wcn
+
l
Bcn
(1)
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where Ccn is the cn-th output of the convolutional layer, and the output number is CN, which is also
equal to the filter number; ∗ is an operator of convolution; Xcc represents the input data of cc-th channel
l is the weight of cn-th filter of the current
of previous layer l − 1, and the channel number is CC; Wcn
layer l; the width and height of the filter are CW and CH, respectively; the cn-th bias is denoted with
l ; f is an activation function, typically hyperbolic tangent or sigmoid function.
bcn
The pooling layer is a sub-sampling layer, which reduces the revolution of the input data and
improves the robustness of learned features. A pooling layer generally follows a convolutional layer
with a max pooling method and it outputs only the maximum of each sub-sampling patch of the
feature maps to subsample the feature maps from the previous convolutional layer. The output can be
described as follows [26]:
Pcn = max Ccn
(2)
Ccn ∈S
where Pcn is the cn-th output of the pooling layer, and the output number is CN; S is the pooling block
size. This function will sum over each distinct S pooling block in the input data so that the output will
become S times smaller along both spatial dimensions.
The fully-connected layer is the last layer of the DCNN model. It follows several combinations
of the convolutional layers and the pooling layers, and classifies the higher-level information from
the previous layers. A fully-connected layer is similar to a traditional multilayer neural network with
a hidden layer and a classification layer, typically using a softmax regression. Assuming that the task
is a K-label problem, the output of the softmax regression can be calculated as follows:



Oj = 

P(y = 1| x; θ )
P(y = 2| x; θ )
...
P(y = k | x; θ )





1



= K

∑ j=1 exp θ ( j) x 

exp θ (1) x
exp θ (2) x
. . . exp θ (K ) x







(3)
where θ (1) , θ (2) , . . . θ (K ) are the parameters of the model, and O j is the final result of the DCNN.
2.2. Training Method
l , and θ ( j) are the learnable parameters
It can be seen from the previous description that wil , bcn
and will be optimized through model training with gradient decent algorithms. Since a gradient
decent algorithm is easily trapped into local optima, we introduce several enhancement methods,
including stochastic gradient decent (SGD), cross-validation, and adaptive learning rate, to solve
this problem. SGD updates gradient [27] based on a few training data instead of the entire training
set. This approach not only increases the training speed, but also improves the training reliability.
Cross-validation selects a validation set from training data to test the performance of the parameters
of the model to avoid overfitting. Since a global constant learning rate easily causes either a slow
convergence with a lower learning rate or a serious fluctuation of the convergence with a higher
learning rate, an adaptive learning rate is employed. The adaptive learning rate has a high rate at
first and decreases with the increase of the training epochs adaptively to obtain a fast and reliable
convergence result.
3. Adaptive Multi-Sensor Data Fusion Method Based on DCNN for Fault Diagnosis
3.1. Procedure of the Proposed Method
An adaptive multi-sensor data fusion method based on DCNN is presented to learn features from
raw data automatically and combine fusion levels adaptively to detect faults of a planetary gearbox.
Through its deep-layered structure, DCNN can fuse input data and extract basic features in the lower
layers, fuse basic features into high level features and decisions in the middle layers, and further fuse
these features and decisions in the higher layers to obtain the final diagnosis result.
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Figure
2 displays the flowchart of the proposed method: (1) four types of signals, including5vibration
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signal [3,28,29], acoustic signal [4,30], current signal [31–33], and instantaneous angular speed (IAS)
(IAS)are
signal
[34,35],according
are selected
to published
studies
and acquired
a planetary
signalspeed
[34,35],
selected
toaccording
published
studies and
acquired
from afrom
planetary
gearbox;
gearbox;
(2) data preprocessing
applied to standardize
each
signal
andthem
divide
them
into segments;
(2) data
preprocessing
is applied toisstandardize
each signal
and
divide
into
segments;
(3) each of
(3) each
of theof
four
of the
fourare
signal
types are
combined
together
simply
one data
the four
segments
thesegments
four signal
types
combined
together
simply
as one
data as
sample
to form
sample
to
form
the
data-level
fused
input
data
of
the
DCNN
model;
(4)
DCNN
is
trained
and
tested
the data-level fused input data of the DCNN model; (4) DCNN is trained and tested with these fused
with these fused input data, and its output will be the diagnosis result of the planetary gearbox. The
input data, and its output will be the diagnosis result of the planetary gearbox. The testing accuracy of
testing accuracy of the output result is used to evaluate the effectiveness of the proposed method. It
the output result is used to evaluate the effectiveness of the proposed method. It should be noted that
should be noted that although we use a data-level fusion in the third step, data is fused again in the
although
we layers
use a of
data-level
fusion
in to
thefurther
third step,
datathe
is fused
again inThe
the DCNN
startingimplicitly
layers of the
starting
the DCNN
model
optimize
data structure.
DCNN
model
to
further
optimize
the
data
structure.
The
DCNN
implicitly
contains
data-level
fusion,
contains data-level fusion, feature-level fusion, and decision-level fusion through the deep-layered
feature-level
decision-level
fusionofthrough
the deep-layered
structure
and ittooptimizes
structure fusion,
and it and
optimizes
a combination
these fusion
levels adaptively
according
the
characteristic
of the fusion
data itself.
a combination
of these
levels adaptively according to the characteristic of the data itself.
Multi-sensor
Accelerometer
Data preprocessing
Data level fusion
Deep convolutional neural networks
based feature learning and data fusion
Divide signals into segments Combine four segments
into one data sample Input data
samples
Convolutional and
pooling layer
Diagnosis result
Fully connected
layer
Microphone
Current sensor
Optical encoder
Figure 2. Flowchart of the proposed method.
Figure 2. Flowchart of the proposed method.
3.2. Model Design of DCNN
3.2. Model Design of DCNN
The model of DCNN is adjusted to satisfy the characteristics of mechanical fault diagnosis.
The
model
DCNN isof DCNN
adjusted
to satisfy
the characteristics
of mechanical
Although
mostofapplications
in image
recognition
chose a 2D convolutional
structure fault
[11,22,36],
and some
researchers
[20,37] of
also
used the
way
to diagnosechose
mechanical
faults, we
diagnosis.
Although
most
applications
DCNN
in same
image
recognition
a 2D convolutional
select[11,22,36],
a one-dimensional
(1D)
convolutional
structure
with the
a 1Dsame
filter way
bank to
as diagnose
the kernelmechanical
of the
structure
and some
researchers
[20,37]
also used
DCNN
model.
In
our
opinion,
the
main
reason
behind
the
applications
of
the
2D
convolutional
faults, we select a one-dimensional (1D) convolutional structure with a 1D filter bank as the kernel of
structure of DCNN in image analysis lies in the natural 2D space correlation in images. However,
the DCNN
model. In our opinion, the main reason behind the applications of the 2D convolutional
most of the measurement data for mechanical fault diagnosis only correlate with time, which is a 1D
structure of DCNN in image analysis lies in the natural 2D space correlation in images. However,
parameter. Thus, 1D convolutional structure should be an appropriate choice for a DCNN-based
most fault
of the
measurement data for mechanical fault diagnosis only correlate with time, which is a 1D
diagnosis problem. In addition, we choose a larger filter size than conventional ones used in
parameter.
Thus,
1D convolutional
be be
anmore
appropriate
choice
for of
a DCNN-based
image recognition.
While a largerstructure
size of theshould
filter may
expensive
in terms
computation, a fault
diagnosis
In addition,
we choosebetween
a largerthe
filter
thanaway
conventional
ones[38],
used
in image
largerproblem.
filter can capture
more information
datasize
farther
from each other
which
recognition.
While
a larger
of the filter
may be more expensive in terms of computation, a larger
may be the
features
in thesize
frequency
domain.
In spite of
the information
many benefitsbetween
of the deep-layered
structure
DCNN,
a “deep”
structure
filter can capture
more
the data farther
awayoffrom
each
other [38],
whichalso
may be
means
complicated
hyper-parameters
as
well
as
various
choices
of
architectures,
which
increases
the
the features in the frequency domain.
difficulty
build
an benefits
appropriate
anddeep-layered
efficient model.
Although
there areaseveral
In
spite oftothe
many
of the
structure
of DCNN,
“deep”researches
structure [39,40]
also means
about the automatic optimization of parameters of DCNN, the optimized process is usually
complicated hyper-parameters as well as various choices of architectures, which increases the difficulty
time-consuming and easily converges into a local optimum due to the large number of parameters of
to build an appropriate and efficient model. Although there are several researches [39,40] about the
DCNN. Thus, we build the DCNN model initially based on a few general design principles [38,41].
automatic
optimization of parameters of DCNN, the optimized process is usually time-consuming and
Then several configurations of the network are tested using the experimental data, and the one with
easilybest
converges
into aislocal
optimum
due toofthe
performance
selected
as the setting
thelarge
finalnumber
model. of parameters of DCNN. Thus, we build
the DCNN model initially based on a few general design principles [38,41]. Then several configurations
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17, 414are tested using the experimental data, and the one with best performance is selected
6 of 15
of
the 2017,
network
as the setting of the final model.
3.3. Comparative Methods
3.3. Comparative
Methods methods are employed to further test and confirm the performance of the
Several comparative
proposed
method.
The flowcharts
comparative
methods
in Figure
Several
comparative
methodsofare
employed to
furtherare
testshown
and confirm
the3.performance of the
To evaluate
ability
of learning
features from
the raw
of the
proposed
proposed
method.the
The
flowcharts
of comparative
methods
aredata
shown
in Figure
3. method, manual
feature
extraction
is
used
as
a
replacement
and
comparison
of
the
feature
learning method,
in each
To evaluate the ability of learning features from the raw data of the proposed
comparative
method.
Eight
time-domain
featuresand
andcomparison
several frequency-domain
features
are
manual
feature
extraction
is used
as a replacement
of the feature learning
in each
extracted.
Root
mean
square
(RMS),
kurtosis,
crest
factor,
skewness,
mean,
minimum,
maximum,
comparative method. Eight time-domain features and several frequency-domain features are extracted.
and variance
are chosen
the handcraft
features
in the time
[34,37,42].
The characteristic
Root
mean square
(RMS),as
kurtosis,
crest factor,
skewness,
mean,domain
minimum,
maximum,
and variance
frequencies
ofthe
the handcraft
planetary features
gearbox [1],
including
the rotating
frequencies
of the sun gear,
planetary
are
chosen as
in the
time domain
[34,37,42].
The characteristic
frequencies
of
gear
and
the
carrier,
the
pass
frequency
of
the
planetary
gear
and
the
meshing
frequency
of
the
the planetary gearbox [1], including the rotating frequencies of the sun gear, planetary gear and
planetary
are selected
the handcraft
features
in the frequency
domain
well as their
ten
carrier,
thegearbox,
pass frequency
of theasplanetary
gear and
the meshing
frequency
of the as
planetary
gearbox,
sidebands
of thefeatures
sensor signals
[34,43,44] except
signal.
For the current
are
selectedfor
asall
thetypes
handcraft
in the frequency
domainfor
asthe
wellcurrent
as their
ten sidebands
for all
signal,ofthe
frequency
its sidebands
generated
the modulation
of thesignal,
characteristic
types
theline
sensor
signalsand
[34,43,44]
except [45]
for the
current by
signal.
For the current
the line
frequenciesand
of its
thesidebands
planetary[45]
gearbox
are chosen
its frequency-domain
features.frequencies
In addition,
frequency
generated
by theas
modulation
of the characteristic
of the
fast
Fourier-transform
(FFT)
energy
of
each
sample,
which
is
obtained
by
splitting
the
frequency
planetary gearbox are chosen as its frequency-domain features. In addition, the fast Fourier-transform
spectrum
of each
sample
into
32 average
bands
and calculating
the RMS
of eachofband
is also
(FFT)
energy
of each
sample,
which
is obtained
by splitting
the frequency
spectrum
each [46],
sample
into
added
intobands
the handcraft
features
in the
domain.
features
32
average
and calculating
the RMS
of frequency
each band [46],
is alsoWhile
addedall
intothe
thedomain
handcraft
featuresare
in
processed
together
as
the
“handcraft
features”,
the
“time-domain
features”
and
“frequency-domain
the frequency domain. While all the domain features are processed together as the “handcraft features”,
features”
are also tested
respectively
to reflect more features”
information
the sensitivity
of the
data.
the
“time-domain
features”
and “frequency-domain
are about
also tested
respectively
to reflect
The comparison
learningof
features
from
data andbetween
the handcraft
features
is marked
more
informationbetween
about thethe
sensitivity
the data.
Theraw
comparison
the learning
features
from
in
orange
in
Figure
3.
raw data and the handcraft features is marked in orange in Figure 3.
(a)
Multi-sensor
Data
preprocessing
(b)
Feature learning
Data
preprocessing
(c)
Raw
data
Handcraft features
DCNN, BPNN or
SVM
Results with manual feature
extraction and data level fusion
DCNN, BPNN or
SVM
Results with feature learning and
feature level fusion
Data
preprocessing
(d)
Feature level
fusion
Feature level
fusion
DCNN, BPNN or
SVM
Results with manual feature
extraction and feature level fusion
Raw data
DCNN, BPNN or
SVM
Decision level
fusion
Results with feature learning and
decision level fusion
Handcraft features
DCNN, BPNN or
SVM
Decision level
fusion
Results with manual feature
extraction and decision level fusion
Raw data
DCNN, BPNN or
SVM
Results with feature learning and
single sensor data
Handcraft features
DCNN, BPNN or
SVM
Results with manual feature
extraction and single sensor data
Feature learning
or
Feature extraction
Feature learning
Data
preprocessing
DCNN or
BPNN
Handcraft features
Feature learning
Feature extraction
Single sensor
Results with feature learning and
data level fusion
Feature learning
or
Feature extraction
Feature extraction
Multi-sensor
DCNN, BPNN or
SVM
Feature learning
or
Feature extraction
Data level fusion
Feature extraction
Multi-sensor
Raw data
Feature learning
Feature learning
or
Feature extraction
Feature extraction
Figure 3. Flowcharts of comparative methods: (a) Data-level fusion methods; (b) Feature-level fusion
with single
single sensory
sensory data.
data.
methods; (c) Decision-level fusion methods; and (d) Methods with
As a comparison of the DCNN model of the proposed method, two intelligent models,
back-propagation neural networks (BPNN) and support vector machine (SVM), are introduced as
replacements of DCNN in each comparative method. BPNN is built into a three-layer model with
Sensors 2017, 17, 414
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Sensorsactivation
2017, 17, 414 functions. SVM uses Gaussian radial basis function (RBF) as the kernel7 function
of 15
sigmoid
and the grid search method to optimize the parameters of the kernel. The three comparative models,
DCNN, BPNN,
and SVM, are
marked
in green
3.
As a comparison
of the
DCNN
model in
ofFigure
the proposed
method, two intelligent models,
For testing the performance
of the
different
fusion levels,
feature-level
fusion
back-propagation
neural networks
(BPNN)
and support
vector manual-selected
machine (SVM), are
introduced as
and replacements
decision-level
explored
and compared
with is
the
data-level
fusion ofmodel
the proposed
of fusion
DCNN are
in each
comparative
method. BPNN
built
into a three-layer
with
sigmoid
functions.
uses
Gaussian
radialfrom
basis raw
function
(RBF)
the kernel
function
method.
Foractivation
feature-level
fusionSVM
with
feature
learning
data,
onlyasDCNN
and
BPNN are
and the
method
to optimize
parameters
of the
kernel.
The three comparative
models,
applied
to grid
learnsearch
features
from
the rawthedata
of each
sensor,
respectively.
The outputs
of the
DCNN,
BPNN,
and
SVM,
are
marked
in
green
in
Figure
3.
second-last layers of DCNN are extracted as the learned features of each sensory data. Then, all the
For testing the performance of the different fusion levels, manual-selected feature-level fusion and
learned features
from the four types of sensors are combined together as the feature-level fused
decision-level fusion are explored and compared with the data-level fusion of the proposed method.
features and used as the input of another DCNN for classification. In the same way, the outputs of
For feature-level fusion with feature learning from raw data, only DCNN and BPNN are applied to
the second-last layers of BPNN are extracted and fused. Then, the fused features are used as the
learn features from the raw data of each sensor, respectively. The outputs of the second-last layers of
inputDCNN
of both
andasSVM
for classification.
Thesensory
comparison
of different
fusion features
levels isfrom
marked
areBPNN
extracted
the learned
features of each
data. Then,
all the learned
in red
in
Figure
3.
the four types of sensors are combined together as the feature-level fused features and used as the
As a comparison
of theformulti-sensory
thethe
proposed
method,
fault diagnosis
input
of another DCNN
classification. input
In the data
same of
way,
outputs of
the second-last
layers ofwith
single
sensory
data is also
applied
to the
evaluate
the effectiveness
of the
proposed
method,
which is
BPNN
are extracted
and fused.
Then,
fused features
are used as the
input
of both BPNN
and SVM
for classification.
The
comparison
of different
fusion levels is marked in red in Figure 3.
marked
in purple and
shown
in Figure
3d.
As a comparison of the multi-sensory input data of the proposed method, fault diagnosis with
single sensory
is also applied to evaluate the effectiveness of the proposed method, which is
4. Experiment
anddata
Discussion
marked in purple and shown in Figure 3d.
4.1. Experiment Setup
4. Experiment and Discussion
An experimental system of a planetary gearbox test rig is established to evaluate the
effectiveness of the proposed method. As shown in Figure 4, it consists of a one-stage planetary
experimental
of a planetary
gearbox
test rig is established
to evaluate
the20-tooth
effectiveness
gearbox, An
a driven
motor system
and a magnetic
brake.
The planetary
gearbox contains
one
sun gear
of
the
proposed
method.
As
shown
in
Figure
4,
it
consists
of
a
one-stage
planetary
gearbox,
a
driven
and three 31-tooth planetary gears, and transmits torque from the sun gear to the planetary carrier of
motor andgears
a magnetic
The planetary
gearbox contains one 20-tooth sun gear and three 31-tooth
the planetary
with brake.
a standstill
ring gear.
planetary gears, and transmits torque from the sun gear to the planetary carrier of the planetary gears
Figure 5 presents the four types of sensors employed in the experiment, including an
with a standstill ring gear.
accelerometer, microphone, current sensor, and optical encoder. Vibration signal, acoustic signal,
Figure 5 presents the four types of sensors employed in the experiment, including an accelerometer,
and motor
current signal are measured by their corresponding sensors and acquired through a data
microphone, current sensor, and optical encoder. Vibration signal, acoustic signal, and motor current
acquisition
box
with a sampling
frequency of
20 kHz
and
data length
points. The
of the
signal are
measured
by their corresponding
sensors
and
acquired
throughofa 320
datakacquisition
boxIAS
with
output
shaft
is
calculated
based
on
counting
the
number
of
high
resolution
pulses
of
the
encoder
a sampling frequency of 20 kHz and data length of 320 k points. The IAS of the output shaft is calculated
[47]; based
and down-sampling,
using of
the
data
acquisition
box
with
the same
frequencyusing
and data
on counting the number
high
resolution
pulses
of the
encoder
[47];sampling
and down-sampling,
theas
data
box
with the same sampling frequency and data length as the other three signals.
length
theacquisition
other three
signals.
4.1. Experiment Setup
Magnetic brake
Motor
Planetary gearbox
Figure
gearboxtest
testrig.
rig.
Figure4.4.Planetary
Planetary gearbox
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Accelerometer
Microphone
Accelerometer
8 of 15
Microphone
Optical encoder
Current sensor
Optical encoder
Current sensor
Figure 5.
5. Four
and their
their installations.
installations.
Figure
Four types
types of
of sensors
sensors and
Figure 5. Four types of sensors and their installations.
Seven health conditions of the planetary gearbox are tested, including normal, pitting tooth,
Seven health conditions of the planetary gearbox are tested, including normal, pitting tooth,
Seven
health
conditions
the crack
planetary
gearbox
tested,
including
normal,
pitting
tooth, in
chaffing
tooth,
chipped
tooth,ofroot
tooth,
slight are
worn
tooth,
and worn
tooth.
As shown
chaffing
tooth,
chipped
tooth,
root
crack
tooth,
slight
worn
tooth,
and
worn
tooth.
As
shown
in
chaffing
chipped
tooth, in
root
tooth, gears.
slight In
worn
tooth,
and worn
tooth.
shown in
Figure
6, alltooth,
the faults
occurred
thecrack
planetary
each
experiment,
only
oneAs
planetary
gear
Figure
6, allall
the
faults
in
planetary
gears.
Ineach
eachexperiment,
experiment,only
only
one
planetary
gear
Figure
the
faultsoccurred
occurred
inthe
planetaryand
gears.
one
planetary
gear
with
one 6,kind
of health
condition
isthe
examined,
allIn
the other
gears are normal.
Experiments
are
with
one
kind
of
health
condition
is
examined,
and
all
the
other
gears
are
normal.
Experiments
are
with
one
kind
of
health
condition
is
examined,
and
all
the
other
gears
are
normal.
Experiments
are
conducted under three motor speeds (600 rpm, 1200 rpm and 1800 rpm) and a load-free condition.
conducted
under three
speeds (600
rpm, 1200
1200rpm
rpmand
and1800
1800rpm)
rpm)and
and
a load-free
condition.
conducted
threemotor
motor
(600
rpm,
aisload-free
condition.
The
detailed under
description
for thespeeds
datasets
and
pattern labels
of the experiment
shown in
Table 1.
The
detailed
description
labels of
ofthe
theexperiment
experimentisisshown
showninin
Table
The
detailed
descriptionfor
forthe
thedatasets
datasets and
and pattern
pattern labels
Table
1. 1.
(a)
(a)
(d)
(d)
(b)
(b)
(e)
(e)
(c)
(c)
(f)
(f)
Figure
tooth; (b)
(b) Chaffing
Chaffingtooth;
tooth;(c)(c)Chipped
Chipped
tooth;
Root
Figure6. 6.Faulty
Faultyplanetary
planetarygears:
gears: (a)
(a) Pitting
Pitting tooth;
tooth;
(d)(d)
Root
Figure 6. Faulty planetary gears: (a) Pitting tooth; (b) Chaffing tooth; (c) Chipped tooth; (d) Root crack
crack
tooth;(e)(e)Slight
Slightworn
worntooth;
tooth;and
and (f)
(f) Worn tooth.
crack
tooth;
tooth.
tooth; (e) Slight worn tooth; and (f) Worn tooth.
Table1.1.Description
Description of
of the pattern
pattern labels
data.
Table
labelsof
ofplanetary
planetarygearbox
gearbox
data.
Table 1. Description of the pattern labels of planetary gearbox data.
PatternLabel
Label Gearbox
Gearbox Condition
Condition Input
Pattern
InputSpeed
Speed(rpm)
(rpm) Load
Load
Pattern
Label
Gearbox
Condition
Input1200
Speed
(rpm)
Load
1
Normal
600,
and
1800
Zero
1
Normal
600, 1200 and 1800 Zero
Normal
600, 1200
and
1800
Pitting
tooth
600,
and
1800
Zero
22 1
Pitting
tooth
600,1200
1200
and
1800 Zero
Zero
2
Pitting tooth
600, 1200
and
1800
Zero
3
Chaffing
tooth
600,
1200
and
1800
Zero
3 3
Chaffing
tooth
600,1200
1200
and
Zero
Chaffing tooth
600,
and
18001800 Zero
4
Chipped
tooth
600,
1200and
and
1800 Zero
Zero
Chipped tooth
600,
1800
4 4
Chipped
tooth
600,1200
1200 and
1800 Zero
55
Root
tooth 600,
1200and
and
1800 Zero
Zero
Rootcracked
cracked tooth
600, 1200
1800
5 6
Root
cracked
tooth 600,
600, 1200
and
1800 Zero
Slightworn
worn tooth
1800
6
Slight
tooth
600, 1200
1200and
and
1800 Zero
Zero
6 7
Slight
worn
tooth
600,1200
1200
and
Zero
Worn
tooth
600,
and
18001800 Zero
7
Worn tooth
600, 1200 and 1800 Zero
7
Worn tooth
600, 1200 and 1800 Zero
4.2.4.2.
Data
Processing
Data
Processing
4.2. Data Processing
The
acquired
signal, current
currentsignal,
signal,and
andIAS
IASsignal
signal
are
preprocessed
The
acquiredvibration
vibrationsignal,
signal, acoustic
acoustic signal,
are
preprocessed
The acquired
vibration
signal,
acoustic
signal,method,
current
signal,
and IAS
signal
are
preprocessed
following
thethe
steps
in in
Section
3.1.
For
the
proposed
the
collected
signals
are
standardized
and
following
steps
Section
3.1.
For
the
proposed
method,
the
collected
signals
are
standardized
following
the
steps
in
Section
3.1.
For
the
proposed
method,
the
collected
signals
are
standardized
divided
into
segments
at
first.
A
total
of
1024
points
are
selected
as
a
segment,
so
each
type
of
signal
and divided into segments at first. A total of 1024 points are selected as a segment, so each type of
and
divided
into
segments
ateach
first.health
Afor
total
of 1024
points
are motor
selected
as aand
segment,
so each
of
will
contain
segments
condition
under
one
speed
6552
segments
in total
signal
will312
contain
312 for
segments
each
health
condition
under
one
motor
speed
and type
6552
signal
willhealth
contain
312seven
segments
for
each
health
under
onefour
motor
speed
6552
segments
in
total
for
healththree
conditions
undercondition
three
motor
speeds.
Next,
each
of the
four
for
seven
conditions
under
motor
speeds.
Next,
each
of the
segments
ofand
the
four
segments
inof
total
for
health
under
three
speeds.
Next,
each
of the
four
segments
the
fourseven
signal
typesasconditions
are
together
asmotor
one input
data
sample
to the
form
the
input
signal
types
are
combined
together
onecombined
data sample
to
form
the
vectors
of
DCNN
model.
vectors
of
the
DCNN
model.
In
this
way,
each
data
sample
will
be
a
4096-dimensional
vector
(four
segments
of
the
four
signal
types
are
combined
together
as
one
data
sample
to
form
the
input
In this way, each data sample will be a 4096-dimensional vector (four times the size of segments),
times of
thethe
sizeDCNN
of segments),
there
willeach
be 6552
samples
A total of 40% ofvector
the 6552
vectors
model. and
In this
way,
datadata
sample
will in
betotal.
a 4096-dimensional
(four
times the size of segments), and there will be 6552 data samples in total. A total of 40% of the 6552
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and there will be 6552 data samples in total. A total of 40% of the 6552 data samples are selected
randomly as the training data set, 10% are used as the validation set, and the remaining 50% are
selected as the testing data set. Eight trails are carried out to avoid particularity and contingency of the
diagnosis result. The average testing accuracy of the eight trails is calculated as the final result.
4.3. Model Design
The model of the DCNN is developed using the principles described in Section 3.2. For different
input data, different model settings are tested, and the one with the best performance among
all the tested settings is selected to process this input data. The model setting of the proposed
method is displayed in Table 2, which consists of three convolutional layers, two pooling layers,
and a fully-connected layer with softmax regression. The convolutional layer corresponds to
Equation (1), the pooling layer to Equation (2) and the fully-connected layer to Equation (3). The DCNN
model is developed based on C++.
Table 2. Variables of parameters and structures of the deep convolutional neural networks.
Layer
Type
Variables and Dimensions
Training Parameters
1
2
Convolution
Pooling
CW = 65; CH = 1; CC = 1; CN = 10; B = 10
S=2
SGD minibatch size = 20
Initial learning rate = 0.05
3
Convolution
CW = 65; CH = 1; CC = 10; CN = 15; B = 15
4
Pooling
S=2
Decrease of learning rate
after each ten epochs = 20%
Momentum = 0.5
5
6
7
Convolution
Hidden layer
Softmax
CW = 976; CH = 1; CC = 15; CN = 30; B = 30
Relu activation function
7 outputs
Weight decay = 0.04
Max epochs = 200
Testing sample rate = 50%
SDG = stochastic gradient decent; CW = filter width; CH = filter height; CC = filter channel; CN = number of filter
in the bank; B = bias; S = sub-sampling rate; No overlapping of convolutional window and no padding.
4.4. Experimental Results
4.4.1. Results of Single Sensory Data
Following the flowchart shown in Figure 3d in Section 3.3, methods with three intelligent models,
feature learning and manual feature extraction methods, and four types of single sensor data are tested
in the experiment. The results are displayed in Table 3.
Table 3. Average testing accuracy of comparative methods with single sensory data.
Sensory Data
Model
Feature Learning
from Raw Data
Vibration signal
DCNN
BPNN
SVM
Acoustic signal
Manual Feature Extraction
Time-Domain
Features
Frequency-Domain
Features
Handcraft
Features
81.45%
42.56%
45.11%
55.84%
55.62%
56.35%
70.74%
69.03%
72.23%
73.64%
72.36%
73.86%
DCNN
BPNN
SVM
66.23%
19.80%
26.54%
31.42%
35.89%
33.62%
76.45%
76.04%
77.36%
76.02%
75.79%
76.32%
Current signal
DCNN
BPNN
SVM
85.68%
52.36%
51.64%
60.73%
60.47%
63.74%
61.45%
61.21%
63.53%
76.85%
76.43%
78.76%
Instantaneous angular
speed (IAS) signal
DCNN
BPNN
SVM
90.23%
51.37%
48.22%
75.34%
75.36%
75.68%
84.42%
85.22%
85.65%
88.34%
89.82%
89.85%
DCNN = deep convolutional neural network; BPNN = back-propagation neural networks; SVM = support
vector machine.
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4.4.2.Results
ResultsofofMulti-Sensory
Multi-SensoryData
Data
4.4.2.
4.4.2. Results of Multi-Sensory Data
Followingthe
theflowcharts
flowchartsshown
shown in
in Figure
Figure 3a–c
3a–c in
in Section
Section 3.3,
Following
3.3, methods
methods with
withthree
threefusion
fusionlevels,
levels,
Following the
flowcharts
shown in
Figure 3a–c
in Section
3.3,
methods with
three
fusion
levels,
three
intelligent
models,
two
feature
extraction
methods,
and
multi-sensory
data
are
tested
three intelligent models, two feature extraction methods, and multi-sensory data are testedin
inthe
the
three intelligent
models,are
twodisplayed
feature extraction
methods,
andthe
multi-sensory
data
are tested
in the is
experiment.
The
results
in
Table
4,
in
which
result
of
the
proposed
method
experiment. The results are displayed in Table 4, in which the result of the proposed method is marked
experiment.
TheFigure
results are
displayed
in Tableresults
4, in which
the
result
of thethe
proposed
method
is
in bold.
presents
the
testing
of the
eight
trails
top three
methods,
inmarked
bold. Figure
7 presents7 the
testing
results
of the eight
trails
of the
topofthree
methods,
which
are
marked
in
bold.
Figure
7
presents
the
testing
results
of
the
eight
trails
of
the
top
three
methods,
which are the proposed method, the DCNN model with feature learning and feature-level fusion,
the proposed method, the DCNN model with feature learning and feature-level fusion, and the SVM
which
the
proposed
DCNNand
model
with feature
learning and feature-level fusion,
and
the are
SVM
model
with method,
handcraftthe
features
feature-level
fusion.
model
with handcraft
features
and feature-level
fusion.
andFinally,
the SVMthe
model
with
handcraft
features
and
feature-level
fusion.
average testing accuracies of all the comparative methods in the experiment are
Finally, thethe
average testing
accuracies
of all
methods
in the
are shown
accuracies
of the
all comparative
the
comparative
methods
inexperiment
the experiment
are
shownFinally,
together inaverage
Figure 8testing
for a clearer
comparison
between
each other.
together
in
Figure
8
for
a
clearer
comparison
between
each
other.
shown together in Figure 8 for a clearer comparison between each other.
Table 4. Average testing accuracy of comparative methods with multi-sensory data.
Table
4. 4.Average
methodswith
withmulti-sensory
multi-sensorydata.
data.
Table
Averagetesting
testingaccuracy
accuracyof
of comparative
comparative methods
Manual Feature Extraction
Feature Learning
Manual
Feature
Manual
Feature Extraction
Extraction Handcraft
Fusion Level
Model Feature Learning Time-Domain
Frequency-Domain
Feature
Learning
from
Raw
Data Time-Domain Frequency-Domain Handcraft
Fusion
Model
Fusion Level
Level
Model
Time-Domain
Frequency-Domain
Handcraft
Features
Features
Features
from
fromRaw
RawData
Data
Features
Features
Features
Features
Features
Features
DCNN
99.28%
66.08%
87.63%
90.23%
DCNN
99.28%
66.08%
87.63%
90.23%
DCNN
99.28%
66.08%
87.63%
90.23%
Data-level fusion
BPNN
53.28%
65.95%
87.89%
91.22%
Data-level
BPNN
53.28%
65.95%
87.89%
91.22%
BPNN
53.28%
65.95%
87.89%
91.22%
Data-level fusion
fusion
SVM
51.62%
67.32%
87.28%
90.67%
SVM
51.62%
67.32%
87.28%
90.67%
SVM
51.62%
67.32%
87.28%
90.67%
DCNN
98.75%
86.35%
92.34%
94.08%
DCNN
98.75%
86.35%
92.34%
94.08%
DCNN
98.75%
86.35%
92.34%
94.08%
Feature-level fusion
BPNN
64.74%
86.81%
92.15%
94.04%
Feature-level
BPNN
64.74%
86.81%
92.15%
94.04%
BPNN
64.74%
86.81%
92.15%
94.04%
Feature-level fusion
fusion
SVM
56.27%
86.74%
94.62%
95.80%
SVM
56.27%
86.74%
94.62%
95.80%
SVM
56.27%
86.74%
94.62%
95.80%
DCNN
93.65%
84.65%
90.23%
92.19%
DCNN
93.65%
84.65%
90.23%
92.19%
DCNN
93.65%
84.65%
90.23%
92.19%
Decision-level
fusion
BPNN
77.62%
84.47%
91.19%
93.42%
Decision-level
BPNN
77.62%
84.47%
91.19%
93.42%
BPNN
77.62%
84.47%
91.19%
93.42%
Decision-level fusion
fusion
SVM
76.17%
86.32%
90.98%
93.44%
SVM
76.17%
86.32%
90.98%
93.44%
SVM
76.17%
86.32%
90.98%
93.44%
Figure
Testing
accuracyof
ofeight
eighttrials
trialsof
the
top
three
the
DCNN
Figure
Testing
accuracy
of
eight
trials
of the
the top
top three
three methods
methods(the
method,
the
DCNN
Figure
7.7.7.
Testing
accuracy
methods
(theproposed
proposedmethod,
method,
the
DCNN
model
with
feature
learning
and
feature-level
fusion,
and
the
SVM
model
with
handcraft
features
modelwith
withfeature
featurelearning
learning
and
feature-level
fusion,
and
SVM
model
with
handcraft
features
model
and
feature-level
fusion,
and
thethe
SVM
model
with
handcraft
features
and
andfeature-level
feature-level
fusion).
FL == feature
feature
learning;
FE
feature
extraction;
and
fusion).
FL
learning;
FE == manual
manual
feature
extraction;
DF= =data-level
data-level
feature-level
fusion).
FL = feature
learning;
FE = manual
feature
extraction;
DF = DF
data-level
fusion;
fusion;
FF==feature-level
feature-level
fusion.
fusion;
FF
FF
= feature-level
fusion. fusion.
The proposed method
The proposed method
Figure 8. Testing accuracy of all the comparative methods.
Figure8.8. Testing
Testingaccuracy
accuracy of
of all
all the
the comparative
comparative methods.
Figure
methods.
Sensors 2017, 17, 414
Sensors 2017, 17, 414
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11 of 15
4.5.
Data and
and Learned
LearnedFeatures
Features
4.5.Principal
PrincipalComponent
ComponentAnalysis
Analysisof
of the
the Experimental
Experimental Data
PCA
toanalyze
analyzeand
and
visualize
learned
features
the proposed
As
PCAisis employed
employed to
visualize
the the
learned
features
of the of
proposed
method.method.
As shown
shown
in
Figure
9,
the
labels
1
to
7
correspond
to
the
seven
conditions
of
the
planetary
gearbox
in Figure 9, the labels 1 to 7 correspond to the seven conditions of the planetary gearbox described
described
Table
andtwo
theprincipal
first two components
principal components
(PCs) arebyobtained
by PCA tothe
represent
in Table 1,inand
the1,first
(PCs) are obtained
PCA to represent
useful
the
useful
information
hidden
in
the
data.
Figure
9a
shows
the
result
of
the
input
data
of
the
testing
information hidden in the data. Figure 9a shows the result of the input data of the testing dataset
of
dataset
of the proposed
method
the PCs.
first two
PCs.
9b illustrates
result
of the
learned
the proposed
method along
thealong
first two
Figure
9bFigure
illustrates
the result the
of the
learned
features
features
with adaptive
fusion
levels
of themethod
proposed
method
testing
the are
dataset,
which
are
with adaptive
fusion levels
of the
proposed
for testing
thefor
dataset,
which
extracted
from
extracted
from
thesecond-last
output of layer
the second-last
layer
the DCNN.the
For
comparison,
the results
the output
of the
of the DCNN.
Forofcomparison,
results
of feature-level
fusedof
feature-level
fused
features
learned
DCNN
andhandcraft
feature-level
fused
handcraft
features learned
through
DCNN
andthrough
feature-level
fused
features
along
the firstfeatures
two PCsalong
are
the
first two
PCs are
shown
in Figure
respectively.
It should
bedisplay
noted that
we only
display
the
shown
in Figure
9c,d,
respectively.
It 9c,d,
should
be noted that
we only
the first
two PCs
of the
first
two
of thevisualization,
data for a clearer
which
that there
may some
be overlaps
between
data
forPCs
a clearer
whichvisualization,
means that there
maymeans
be overlaps
between
categories
and
some
categories
andbemany
of them
can bePCs.
divided into higher PCs.
many
of them can
divided
into higher
(a)
(b)
(c)
(d)
Figure
component
analysis
(PCA)
of of
thethe
experimental
data
and
learned
features:
(a)
Figure9.9.Principal
Principal
component
analysis
(PCA)
experimental
data
and
learned
features:
Data-level
fusedfused
inputinput
datadata
of the
proposed
method;
(b) (b)
Features
of the
proposed
method;
(c)
(a) Data-level
of the
proposed
method;
Features
of the
proposed
method;
Feature-level
fused
features
learned
through
DCNN;
features.
(c) Feature-level
fused
features
learned
through
DCNN;and
and(d)
(d)Feature-level
Feature-levelfused
fused handcraft
handcraft features.
4.6.
4.6.Discussion
Discussion
1.1. The
is able
able to
to diagnose
diagnosethe
thefaults
faultsofofthe
the
Theexperimental
experimentalresults
resultsshow
showthat
thatthe
the proposed
proposed method
method is
planetary
best testing
testingaccuracy
accuracyininthe
theexperiment.
experiment.ItItcan
can
planetarygearbox
gearboxtest
testrig
rigeffectively,
effectively, yielding the best
bebeseen
from
Table
4
and
Figure
8
that
the
proposed
method
achieves
the
best
testing
accuracy
seen from Table 4 and Figure 8 that
method achieves the best testing accuracy
99.28%
this result
resultisissignificantly
significantlycorrelated
correlated
99.28%among
amongall
allthe
thecomparative
comparativemethods.
methods. We
We think that this
with
proposedmethod.
method.DCNN
DCNNcan
canfuse
fuseinput
input
withthe
thedeep
deeparchitecture
architectureof
ofthe
the DCNN
DCNN model
model of the proposed
dataand
andlearn
learnbasic
basicfeatures
featuresfrom
from itit in
in its
its lower layers, fuse
data
fuse basic
basic features
featuresinto
intohigher
higherlevel
level
featuresorordecisions
decisionsininits
itsmiddle
middlelayers,
layers, and
and further
further fuse these
features
these features
featuresand
anddecisions
decisionstotoobtain
obtain
thefinal
finalresult
resultininits
itshigher
higher layers.
layers. Although
Although there
there is a data-level
the
data-level fusion
fusionbefore
beforeDCNN
DCNNininthe
the
proposedmethod,
method,DCNN
DCNNstill
stillactually
actually fuses
fuses the
the data again in
proposed
in its
its starting
starting layers
layerstotofurther
further
optimize the data structure. Optimized features and combinations of different level fusions are
formed through this deep-layered model, which provides a better result than with manually
selected features or fusion levels.
Sensors 2017, 17, 414
2.
3.
4.
5.
6.
12 of 15
optimize the data structure. Optimized features and combinations of different level fusions are
formed through this deep-layered model, which provides a better result than with manually
selected features or fusion levels.
The ability of automatic feature learning of the DCNN model with multi-sensory data is proven
through the experiment. It can obviously be seen from Figure 8 that both the proposed method
and the feature-level fusion method with feature learning through DCNN obtain a better result,
99.28% and 98.75%, than any other comparative methods with handcraft features or feature
learning through BPNN. This result proves that the feature learning through DCNN with
multi-sensory data can improve the performance of the multi-sensor data fusion method for
fault diagnosis. In addition, the result also implies that the proposed method with adaptive
fusion-level selection can achieve a better result 99.28% than the result 98.75% of the method with
manual-selected feature-level fusion, which is the only difference between these two methods.
However, the method with automatic feature learning of DCNN from the raw signal of a single
sensor cannot achieve a better result than methods with handcraft features. Table 3 displays the
diagnosis results using signals from a single sensor. Only with a vibration signal and current
signal, can the DCNN-based feature learning method achieve better results than conventional
methods with handcraft features. By contrast, the results of the DCNN-based feature learning
method with an acoustic signal and IAS signal are worse than that of conventional methods.
This implies that the DCNN-based method with learned features from single sensory data cannot
provide stable improvements for all kinds of sensory data. We think that the performance of the
DCNN-based feature learning is influenced by the characteristics of the input data. As can be
seen from the results shown in Table 3, the performance of feature learning has a stronger positive
correlation with the performance of time-domain features than frequency-domain features,
which infers that the DCNN-based feature learning from a raw signal may be more sensitive to
time-correlated features than frequency-correlated features.
The effectiveness of the automatic feature learning and adaptive fusion-level selection of the
proposed method is further confirmed through PCA. As can be seen from Figure 9a, most of the
categories of the input raw data overlap each other, which makes it difficult to distinguish them.
After the processing of the proposed method, the learned features with adaptive fusion levels
along the first two PCs become distinguishable in Figure 9b. Meanwhile, Figure 9c,d presents the
results of PCA with feature-level fused learned features and handcraft features as comparisons,
respectively. The feature-level fused features learned through DCNN have just a slightly worse
distinction between each category than the features of the proposed method, which not only
verifies the feature learning ability of DCNN used in both methods, but also proves the better
performance of the adaptive-level fusion of the proposed method than that of the manual-selected
feature-level fusion. On the contrary, the fused handcraft features show a much worse distinction
between different categories than the learned features of the proposed method. These analyses
further demonstrate the effective performance of the automatic feature learning and adaptive
fusion-level selection of the proposed method.
While DCNN has a much better feature learning ability than BPNN, the three comparative models,
DCNN, BPNN and SVM, obtain similar results with handcraft features. Figure 8 shows clearly
that feature learning through DCNN achieves much better testing accuracies than through BPNN.
Nevertheless, with handcrafts features, these three intelligent models provide similar accuracies,
which suggests that DCNN cannot achieve much more improvements than conventional methods
without using its ability of feature learning.
Methods with multi-sensory data provide better results than those with single sensory data. It can
be seen from Figure 8 that methods with multi-sensory data achieve higher testing accuracies
than with single sensory data, no matter which fusion level or intelligent model is selected.
This phenomenon indicates that multi-sensory data can improve the reliability and accuracy for
fault diagnosis.
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5. Conclusions and Future Work
This paper presents an adaptive data fusion method based on DCNN to detect the health conditions
of planetary gearboxes. The processes of data-level fusion, feature-level fusion, decision-level fusion,
feature learning, and fault diagnosis are all fused into one DCNN model adaptively. The proposed
method can learn features from raw data, and fuse data, features, and decisions adaptively through the
deep-layered structure of DCNN with fewer requirements of expert knowledge or human labor for
feature extraction and fusion-level selection. The performance of the proposed method is evaluated
through the experiment of the planetary gearbox fault test rig. As comparisons, feature-level fusion,
decision-level fusion, handcraft features, single sensory data, and two traditional intelligent models,
BPNN and SVM, are also tested in the experiment. The comparative results of the experiment verify
the effectiveness of the proposed method, which achieves the best testing accuracy among all the
comparative methods in the experiment.
Our future work will focus on testing the DCNN model-based feature learning and data fusion
approaches on more mechanical objects, fault modes, operation conditions, and sensor types, which can
further confirm the effectiveness of approaches and help us to find out other useful application
guidance. Moreover, due to the large number of parameters of deep learning models, manual parameter
optimization often takes many trials-and-errors to find the best one, and conventional automatic
searching methods are usually very time-consuming and easily converge into a local optimum.
It is meaningful to investigate more effective and faster approaches to optimize the parameters
automatically. Finally, combinations of different deep learning architectures should improve the effect
of fault diagnosis. Adding recurrent architecture may make the model suitable to predict future fault
conditions, and combining with auto-encoder architecture may improve the feature learning ability to
capture more complex features.
Acknowledgments: The authors would like to thank the National Natural Science Foundation of China
(Grant No. 51475324), and the National Natural Science Foundation of China and Civil Aviation Administration
of China jointly funded project (Grant No. U1533103) for their support. The authors would also like to thank the
editors and anonymous reviewers for their constructive comments and suggestions.
Author Contributions: Luyang Jing and Taiyong Wang conceived and designed the research; Ming Zhao designed
and performed the experiment; Peng Wang contributed to the theory studies; Luyang Jing wrote the manuscript;
and all the authors reviewed and revised the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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