Transformdomain sparse representation based classification for machinery vibration signals
Yu Fajun^{1} , Fan Fuling^{2} , Wang Shuanghong^{3} , Zhou Fengxing^{4}
^{1, 2, 3}School of Electric and Information Engineer, Zhongyuan University of Technology, Zhengzhou, Henan, 450007, P. R. China
^{4}School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan, Hubei, 430081, P. R. China
^{1}Corresponding author
Journal of Vibroengineering, Vol. 20, Issue 2, 2018, p. 979987.
https://doi.org/10.21595/jve.2017.18865
Received 18 July 2017; received in revised form 10 October 2017; accepted 22 October 2017; published 31 March 2018
JVE Conferences
The working state of machinery can be reflected by vibration signals. Accurate classification of these vibration signals is helpful for the machinery fault diagnosis. A novel classification method for vibration signals, named Transform Domain Sparse Representationbased Classification (TDSRC), is proposed. The method achieves high classification accuracy by three steps. Firstly, timedomain vibration signals, including training samples and test samples, are transformed to another domain, e.g. frequencydomain, waveletdomain etc. Then, the transform coefficients of the training samples are combined as a dictionary and the transform coefficients of the test samples are sparsely coded on the dictionary. Finally, the class label of the test samples is identified by their minimal reconstruction errors. Although the proposed method is very similar to the Sparse Representationbased Classification (SRC), experimental results illustrates its performance is far superior to SRC in the classification of vibration signals. These experiments include: frequencydomain classification of bearing vibration data from the Case Western Reserve University (CWRU) Bearing Data Center and waveletdomain classification of six faulttypes gearbox vibration data from our rotating machinery experimental platform.
Keywords: sparse representationbased classification; transform domain; vibration signal; fault diagnosis.
1. Introduction
In the field of machinery fault diagnosis, vibration analysis is one of the most common and reliable methods [1]. It takes advantage of the advanced signal processing methods to extract fault information from raw vibration signals, which are collected by vibration sensors installed on the machinery, and then makes a diagnosis according to the fault information. In the past few decades, Fourier transform (FT) [2, 3] and wavelet transform (WT) [4, 5] were widely utilized in recognizing the fault feature frequencies of machinery equipment with lots of decent results. However, the feature frequency cannot be known in some cases for the difficulty in obtaining the rotating frequency or parameters of mechanical parts, which limits its broader application.
Classificationbased fault diagnosis, as another method of vibration analysis, uses training samples to establish a diagnostic decision maker and determines the fault type of test samples according to the maker output, which avoids the calculation of fault feature frequencies. Frequently used classification methods in the field of fault diagnosis include linear discriminant analysis (LDA) [6], artificial neural network (ANN) [7] and support vector machine (SVM) [8, 9], etc. LDA, as a basic Fisher discriminant classifier, pursues a low degree of coupling between classes and a high degree of polymerization within class. ANN realizes nonlinear mapping between symptoms and faults. SVM, as another linear classifier, is a machine learning method based on statistical learning theory, and produces a favorable generalization performance. In addition, the classification method for timedomain parameters and fuzzy logic classification techniques [10] were also used well in the area of fault diagnosis.
In recent years, a new classification technique, i.e. sparse representation based classification (SRC), has been proposed in the field of pattern recognition [11, 12]. Its basic principle is to sparse code a test sample over a dictionary and then to perform the classification based on the reconstruction error. Since its appearance, SRC and its variants have been widely applied in face recognition [13, 14], EEG signal classification [15] and music genre classification [16] etc. In the field of fault diagnosis, SRC is rarely studied. A typical application appeared with a good result in [17], where compressive sensing theory was implied to reduce the dimension of original vibration signals and SRC was used to classify the low dimensional signals.
In this paper, on the basis of SRC, we propose a new classification method for machinery vibration signals, named Transform Domain Sparse Representationbased Classification (TDSRC). In TDSRC, the dictionary for sparse representation is not constructed with raw samples, but constructed with the transformation coefficients of the raw samples. This provides a new idea that classification can be performed using sample variations in the transform domain
The idea comes from the fact that machinery vibration signals of different faulttypes show significant differences in transform domain. Compared to previous studies, the method of TDSRC makes better use of the global differences between different sample classes. The results of two experiments demonstrate that the classification performance of the proposed method, i.e. classification accuracy, sparsity concentration index [12] and noise immunity, is better than that of SRC and conventional SVM.
The remainder of the paper is organized as follows. In Section 2, the basic idea of SRC and its variants are reviewed. Section 3 presents the proposed method TDSRC. Two experiments are provided to verify the proposed method in Section 4. Finally, Section 5 concludes the paper.
2. Sparse representationbased classification (SRC) and its variants
Suppose $\mathbf{A}=\left[{\mathbf{A}}_{1},{\mathbf{A}}_{2},\cdots ,{\mathbf{A}}_{K}\right]\in {R}^{m\times n}$ is a training dataset as the concatenation of $n$ training samples of all $K$ object classes, and ${\mathbf{A}}_{k}=\left[{\mathbf{v}}_{k,1},{\mathbf{v}}_{k,2},\cdots ,{\mathbf{v}}_{k,{n}_{k}}\right]\in {R}^{m\times {n}_{k}}$ is the subset of the training samples from class $k$. For a test sample $\mathbf{y}\in {R}^{m}$from class $k$, generally it can be well approximated as the linear combination of the samples from ${\mathbf{A}}_{k}$ [12, 18]:
where ${\mathbf{\alpha}}_{k}={\left[{\mathbf{\alpha}}_{k,1},{\mathbf{\alpha}}_{k,2},\cdots ,{\mathbf{\alpha}}_{k,{n}_{k}}\right]}^{T}\in {R}^{{n}_{k}}$ is the coding vector. Since the membership $k$ of the test sample is initially unknown, the linear representation of $\mathbf{y}$ can be written in terms of all training samples as $\mathbf{y}=\mathbf{A}\mathbf{\alpha}$, where:
In SRC [10], L1norm minimization is used to sparsely code $\mathbf{y}$ on $\mathbf{A}$, i.e.:
where $\gamma $ is a scalar constant. Then classification is done via:
where ${e}_{k}={\Vert \mathbf{y}{\mathbf{A}}_{k}{\widehat{\mathbf{\alpha}}}_{k}\Vert}_{2}$, and ${\widehat{\mathbf{\alpha}}}_{k}$ is the coefficient vector associated with class $k$. Moreover, other sparse optimization criterions were recently utilized in classification issue. For example, L2norm minimization, named Collaborative Representation (CR), proposed by Zhang et al. [13] solves the coding vector by:
L1norm combining with L2norm minimization, named Class Specific Sparse Representation (CSSR), was presented by Huang et al. [19] formulated as:
and classification is made by:
where ${e}_{k}={\Vert \mathbf{y}{\mathbf{A}}_{k}{\widehat{\mathbf{\alpha}}}_{k}\Vert}_{2}/{\Vert {\widehat{\mathbf{\alpha}}}_{k}\Vert}_{2}$.
3. Transform domain sparse representationbased classification (TDSRC)
In section 2, it can be seen that there are two phases in SRC: Sparse coding and classification. In the coding phase, training samples are combined together as a dictionary. In recent years, lots of methods for constructing dictionary have appeared, such as FDDL [18], JDDLDR [20] and DKSVD [21] etc. These methods have one thing in common: the dictionary is constructed by a learning algorithm to improve its discrimination ability as much as possible. Although the dictionary is updated in each iteration, it keeps in the same domain as training samples. It is well known that machinery vibration signal is noisy and nonstationary, which means that its many statistics are timevarying. Therefore, even if different vibration signals are of the same faulttype, they will show significant differences in timedomain waveform. However, they have many nearly the same statistics in transform domain. For example, bearing vibration signals of the same faulttype have almost the same dominate frequencies in frequencydomain, and gear vibration signals of the same faulttype almost have the same wavelet coefficients in waveletdomain.
Table 1. Algorithm of TDSRC
Input: a matrix of training samples $A=\left[{\mathbf{v}}_{1},{\mathbf{v}}_{2},\cdots ,{\mathbf{v}}_{n}\right]\in {R}^{m\times n}$ for $K$ classes, a test sample
$\mathbf{y}\in {R}^{m}$, a transformation $T[\cdot ]$, a maximum transform scale $L$ and a scalar constant $\gamma $

Step 1: For every training sample ${\mathbf{v}}_{i}\mathrm{}\mathrm{}(i=1,\mathrm{}\mathrm{}2,\dots ,n)$, perform $T\left[{\mathbf{v}}_{i}\right]$ to obtain a coefficient matrix ${\mathbf{C}}_{i}$, whose columns are the transform coefficient vectors of ${\mathbf{v}}_{i}$ from scale 1 to $L$. The same as the test sample $\mathbf{y}$, perform $T\left[\mathbf{y}\right]$ to obtain a coefficient matrix $\mathbf{Y}$

Step 2: For $j=1,\mathrm{}\mathrm{}2,\dots ,L$

Step 2.1: Let dictionary ${D}_{j}=[{C}_{1,j},{C}_{2,j},\cdots ,{C}_{n,j}]$, where ${C}_{i,j}(i=1,2,\cdots ,n)$ denotes the
$j$th column vector of ${\mathbf{C}}_{i}$, i.e. the transform coefficient vector of ${\mathbf{v}}_{i}$ at scale $j$

Step 2.2: Normalize the columns of ${\mathbf{D}}_{j}$to have unit L2norm; where ${\mathbf{D}}_{j,k}$ and ${\widehat{\mathbf{\alpha}}}_{j,k}$ are the subdictionary of ${\mathbf{D}}_{j}$ and subvector of ${\widehat{\mathbf{\alpha}}}_{j}$ associated with class $k$ respectively, and they meet ${\mathbf{D}}_{j}=[{\mathbf{D}}_{j,1},\cdots ,{\mathbf{D}}_{j,k},\cdots ,{\mathbf{D}}_{j,K}]$ and ${\widehat{\mathbf{\alpha}}}_{j}=[{\widehat{\mathbf{\alpha}}}_{j,1};\cdots ;{\widehat{\mathbf{\alpha}}}_{j,k};\cdots ;{\widehat{\mathbf{\alpha}}}_{j,K}]$

Output: $identity\left(y\right)=arg\underset{k}{min}\left\{{R}_{k}\right\}$

Based on the above considerations, in the proposed method, i.e. TDSRC, the vibration signals, including training samples and test samples, are transformed to another domain at first; then, sparse representationbased classification is performed in the transform domain. The algorithm of TDSRC is summarized in Table 1. It needs three steps to finish the algorithm. In the first step, all the samples, including training samples and test samples, are transformed into another domain. In the second step, the transform coefficients of the training samples are combined as a dictionary and the transform coefficients of the test samples are sparsely coded on the dictionary. In the final step, the class label of the test samples is identified by their minimal reconstruction errors. In Table 1, if the type of transformation is specified as Fouriertransform, the input parameter $L$ represents the points of discrete Fourier transform (DFT) and the coefficients matrix ${\mathbf{C}}_{i}$ degenerates into a vector composed of DFT coefficients; and if wavelettransform (WT) is used, the wavelet name must be given and ${\mathbf{C}}_{i}$ is composed of WT coefficient vectors with different length.
Step 2.3: Solve the L1minimization problem:
where ${\mathbf{Y}}_{j}$ is the $j$th column vector of $\mathbf{Y}$, i.e. the transform coefficient vector of $\mathbf{y}$ at scale $j$;
Step 2.4: Compute the residuals:
End for Step 3: Compute the summation residuals:
4. Experimental verification
To investigate the performances of the proposed TDSRC method for vibration signal classification, two experimental cases, i.e. bearing vibration signals and gearbox vibration signals, are considered in this section.
4.1. Bearing vibration signal classification
The bearing vibration data were downloaded from the Case Western Reserve University Bearing Data Center [22]. The experimental platform shown in Fig. 1 consists of a motor, control electronics, a torque transducer, and a dynamometer. Single point faults of size 0.007, 0.014, 0.021 and 0.028 in. were set on the driveend bearings (Type 62052RS JEM SKF) at the location of outer raceway, inner raceway and rolling element (ball), respectively. The vibration data were measured by using an accelerometer being attached to the motor housing with the sampling frequency of 12 kHz.
Fig. 1. Bearing test rig of CWRU [22]
In the preprocessing stage, twelve faulttypes of vibration data samples were chosen to construct training and testing datasets, including a normal, three types of outer race fault, four types of inner race fault, and four types of ball fault. Each sample was split with an overlapping length of 128point into lots of segments, whose length were set to 2048point. A total of over 80 segments can be obtained. Taking into account the influence of sample size, the number of training samples must be large enough to build the dictionary for sparse decomposition. Therefore, we randomly selected more than 60 segments as training samples and other 20 ones as testing samples. The descriptions of the bearing datasets are shown in Table 2.
Table 2. Description of bearing dataset for classification
Fault type

Data file

Number of training/testing samples

Label of class

Normal

Normal_3

60/20

N

Outerrace

OR007@6_3

60/20

OI

Outerrace

OR014@6_3

60/20

OII

Outerrace

OR021@6_3

60/20

OIII

Innerrace

IR007_3

60/20

II

Innerrace

IR014_3

60/20

III

Innerrace

IR021_3

60/20

IIII

Innerrace

IR028_3

60/20

IIV

Ball

B007_3

60/20

BI

Ball

B014_3

60/20

BII

Ball

B021_3

60/20

BIII

Ball

B028_3

60/20

BⅣ

In the implementation of TDSRC, all the samples, including 720 (60×12) training and 240 (20×12) testing ones, are transformed into frequencydomain by Fast Fourier Transform (FFT) at first. Then, the scalar constant$\gamma $ is set to 0.5 and SLEP [23] method is utilized for solving L1minimization problems. With the changes of FFT points, the classification accuracy rate is plotted in Fig. 2. It can be seen that the accuracy rate is improved significantly with the increase of FFT points. Fig. 3 shows the relationship between the classification accuracy rates，obtained respectively by TDSRC and SRC，and the scalar constant$\gamma $ with the FFT points of 2048, which demonstrates that both of them almost keep unchanged with high values when the parameter $\gamma $ varies in [0.0001, 0.8], while drop sharply if $\gamma $ approaches to 0 or 1. At the same time, it is verified that the classification performance of TDSRC is superior to that of SRC for the bearing dataset.
Fig. 2. The relationship between the classification accuracy rate and FFT points for the bearing dataset
Fig. 3. The relationship between the classification accuracy rates obtained respectively by TDSRC and SRC and the scalar constant $\gamma $ for the bearing dataset
4.2. Gearbox vibration signals classification
The proposed TDSRC method is further applied in gearbox fault diagnosis. The experimental platform (Fig. 4) consists of a motor, a drive shaft seat, a magnetic particle torque converter and a gearbox, etc. The vibration signals are acquired by an acceleration sensor placed in the output shaft bearing seat. A normal situation and five faultones, including three single faults, i.e. toothbroken and pointcorrosion of large gear, wearout of small gear, and two combination faults, i.e. brokenwear and pointwear faults, are considered. Rotating speed is set to 1500 r/min, and vibration signals of the horizontal direct are collected with a sampling frequency of 5120 Hz and the sampling time is 10.5 s in each situation.
Like the experiment of bearing data classification, in the preprocessing stage, each of the six gearing vibration signals is split with an overlapping length of 128point into lots of segments, whose length are set to 1024point. A total of over 70 segments can be obtained. From them, we randomly select more than 50 ones as training samples to build the dictionary and other 20 ones as testing samples. The descriptions of the gearbox datasets are shown in Table 3.
Fig. 4. The gearbox experimental system
Table 3. Description of gearbox dataset for classification
Fault type

Number of training/testing samples

Label of class

Normal

50/20

N

Toothbroken in large gear

50/20

TBL

Pointcorrosion in large gear

50/20

PCL

Wearout in small gear

50/20

WOS

Toothbroken in large gear and
wearout in small gear

50/20

TBLWOS

Pointcorrosion in large gear and
wearout in small gear

50/20

PCLWOS

It is well known that wavelet transform (WT) has perfect local properties in both time and frequency spaces. However, WT does not split the high frequency bands. Wavelet packets transform (WPT) [24] further decomposes the high frequency part which is not decomposed in WT. Since the modulation information of machine fault always exists in the high frequency bands, in this respect, WPT has a better representation of fault signal [25]. Therefore, all the samples, including 300 (50×6) training and 120 (20×6) testing ones, are transformed into waveletdomain by WPT at first. Then, the scalar constant $\gamma $ is set to 0.5 and TDSRC is implemented. The classification accuracy rate is shown in Fig. 5. The results indicate: 1) as the decomposition depth increases, all of the classification accuracy rates increase at first and then decrease; 2) in the four wavelet packets (‘db1’, ‘coif1’, ’dmey’ and ’sym2’), ‘sym2’ has the highest accuracy, reaching to 95.3 % when the transform depth is 4. To be clear, their accuracy rates are only 27.4 % at the transform depth of 0 for the four wavelet packets, which means that WPT is not conducted and SRC is directly implemented to classify the 120 testing samples. Therefore, SRC in waveletdomain can significantly improve the accuracy of fault diagnosis for gearbox.
For comparison, the gearbox dataset is tested by SVM method. SVM is a pattern recognition classification algorithm based on statistical learning theory and originally designed for binary classification. Fault diagnosis is a multiclassification problem, thus multiclassification SVM should be constructed. Modeled on reference [26], the gearbox dataset, including 300 training samples and 120 testing ones, are translated into different frequency bands by WPT at first; then, the optimal features are selected based on the distance evaluation technique from the statistical characteristics of raw signals and wavelet package coefficients, and the energy characteristics of decomposition frequency band; finally, the optimal features are input a multiclassification SVM with SVM toolbox [27] to classify these samples. The relationship between classification accuracy and number of classifiers is plotted in Fig. 6, which demonstrates that the classification performance is not as high as that of the proposed TDSRC method for gearbox dataset.
Fig. 5. The relationship between the classification accuracy rate and decomposition depth for the gearbox dataset
Fig. 6. The relationship between the classification accuracy and the number of classifiers for the gearbox dataset
4.3. Discussion
In the two experiments above, Fourier transform and Wavelet packets transform were implemented respectively before classification. It is vital to choose a proper transformation for the classification accuracy. In the diagnosis experiment of bearing, FT was chosen because the distribution of Fourier coefficients is obviously different when the bearing is in different faults. In the gearbox experiment, however, WPT was implemented to get WPCs of different frequency bands for their difference of energy distribution when the gearbox is in different faults. Therefore, it needs to get a thorough knowledge of coefficients distribution of the classification objects in the transform domain before implementation of TDSRC.
In addition to the choice of transformation, these parameters have also some influence on classification accuracy, such as the number of FFT points, the scalar constant$\gamma $, the decomposition depth of WPT and the number of training samples. The influence results of the first three parameters have been shown respectively in Fig. 2, Fig. 3 and Fig. 5. Considering the influence of the number of training samples, we selected different numbers of training samples to construct the dictionary for spare decomposition, and selected other 20 samples of each fault type for test. The classification accuracy rates of the bearing dataset and the gearbox dataset are shown in Fig. 7.
Fig. 7. The relationship between the classification accuracy and the number of training samples
It can be seen that both of them increase at first and then decrease. For the bearing dataset, the highest accuracy reaches to 98.4 % when the number of training samples is 60. For the gearbox dataset, the highest accuracy gets to 96.1 % when the numbers of training samples are 50. Therefore, the number of training samples must be appropriate. The information of constructed dictionary is incomplete when the number is too small. On the other hand, redundant information will be generated if the number is too large.
5. Conclusions
This paper presents a new classification method for machinery vibration signals, named Transformdomain Sparse Representation based Classification (TDSRC). It provides a new idea that classification can be performed using sample variations in the transform domain, which greatly improves the flexibility of sample classification. The method leverages the fact that machinery vibration signals possess sparse nonzero values in transformdomain and their transform coefficients are significantly different with each other when different fault occurs. The experimental results of bearing and gearbox vibration signals demonstrate the method can effectively diagnose both of them fault types with a higher accuracy than that of SRC and SVM.
Acknowledgements
This work is partially supported by the National Natural Science Foundation of China (No. 61174106) and the Key Scientific Research Project of Henan Education Department (No. 18B510020).
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