Bearing fault diagnosis via kernel matrix construction based support vector machine

A novel approach on kernel matrix construction for support vector machine (SVM) is proposed to detect rolling element bearing fault efficiently. First, multi-scale coefficient matrix is achieved by processing vibration sample signal with continuous wavelet transform (CWT). Next, singular value decomposition (SVD) is applied to calculate eigenvector from wavelet coefficient matrix as sample signal feature vector. Two kernel matrices i.e. training kernel and predicting kernel, are then constructed in a novel way, which can reveal intrinsic similarity among samples and make it feasible to solve nonlinear classification problems in a high dimensional feature space. To validate its diagnosis performance, kernel matrix construction based SVM (KMCSVM) classifier is compared with three SVM classifiers i.e. classification tree kernel based SVM (CTKSVM), linear kernel based SVM (L-SVM) and radial basis function based SVM (RBFSVM), to identify different locations and severities of bearing fault. The experimental results indicate that KMCSVM has better classification capability than other methods.

[1]  Zhao Zhang,et al.  Fault diagnosis of rolling element bearings via discriminative subspace learning: Visualization and classification , 2014, Expert Syst. Appl..

[2]  Bing Li,et al.  Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform , 2015 .

[3]  Farhat Fnaiech,et al.  Application of higher order spectral features and support vector machines for bearing faults classification. , 2015, ISA transactions.

[4]  Idriss El-Thalji,et al.  A summary of fault modelling and predictive health monitoring of rolling element bearings , 2015 .

[5]  Min-Chun Pan,et al.  An insight concept to select appropriate IMFs for envelope analysis of bearing fault diagnosis , 2012 .

[6]  Guangming Dong,et al.  A frequency-shifted bispectrum for rolling element bearing diagnosis , 2015 .

[7]  Pingfeng Wang,et al.  A tri-fold hybrid classification approach for diagnostics with unexampled faulty states , 2015 .

[8]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[9]  Fengshou Gu,et al.  A novel procedure for diagnosing multiple faults in rotating machinery. , 2015, ISA transactions.

[10]  Lei Deng,et al.  Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine , 2014 .

[11]  Cheng Guo,et al.  Investigation on early fault classification for rolling element bearing based on the optimal frequency band determination , 2015, J. Intell. Manuf..

[12]  V. Rai,et al.  Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform , 2007 .

[13]  Baoping Tang,et al.  Rotating machine fault diagnosis using dimension reduction with linear local tangent space alignment , 2013 .

[14]  J. Rafiee,et al.  A novel technique for selecting mother wavelet function using an intelligent fault diagnosis system , 2009, Expert Syst. Appl..

[15]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[16]  Brigitte Chebel-Morello,et al.  Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network , 2015 .

[17]  Yang Yu,et al.  A fault diagnosis approach for roller bearings based on EMD method and AR model , 2006 .

[18]  B. Samanta,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES , 2003 .

[19]  M. S. Safizadeh,et al.  Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell , 2014, Inf. Fusion.

[20]  Chun-Chieh Wang,et al.  Applications of fault diagnosis in rotating machinery by using time series analysis with neural network , 2010, Expert Syst. Appl..

[21]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .

[22]  Abdolreza Ohadi,et al.  Comparison of FDA-based and PCA-based features in fault diagnosis of automobile gearboxes , 2013, Neurocomputing.

[23]  Jyoti K. Sinha,et al.  An improved data fusion technique for faults diagnosis in rotating machines , 2014 .

[24]  P. Konar,et al.  Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) , 2011, Appl. Soft Comput..

[25]  Suraj Prakash Harsha,et al.  Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN , 2013, Expert Syst. Appl..

[26]  Zhiqi Fan,et al.  A hybrid approach for fault diagnosis of planetary bearings using an internal vibration sensor , 2015 .

[27]  Rong Jiang,et al.  A novel approach to wavelet selection and tree kernel construction for diagnosis of rolling element bearing fault , 2017, J. Intell. Manuf..

[28]  Mohammad Esmalifalak,et al.  A data mining approach for fault diagnosis: An application of anomaly detection algorithm , 2014 .

[29]  Rong Jiang,et al.  ANN Based Multi-classification Using Various Signal Processing Techniques for Bearing Fault Diagnosis , 2015 .

[30]  Joseph Mathew,et al.  A review on prognostic techniques for non-stationary and non-linear rotating systems , 2015 .

[31]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using continuous wavelet transform , 2011, Appl. Soft Comput..

[32]  Xiaoming Xue,et al.  An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis , 2015 .

[33]  Meng Gan,et al.  Multiple-domain manifold for feature extraction in machinery fault diagnosis , 2015 .

[34]  Chuan Li,et al.  A generalized synchrosqueezing transform for enhancing signal time-frequency representation , 2012, Signal Process..

[35]  K. I. Ramachandran,et al.  Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM) , 2010, Appl. Soft Comput..

[36]  N. Tandon,et al.  Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and Duffing oscillator , 2012 .

[37]  Hugo Jair Escalante,et al.  Detection of defective embedded bearings by sound analysis: a machine learning approach , 2014, Journal of Intelligent Manufacturing.