Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposed GA-based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

[1]  Wei He,et al.  Rolling element bearing fault detection based on optimal antisymmetric real Laplace wavelet , 2011 .

[2]  H. W. Ngan,et al.  Detection of Motor Bearing Outer Raceway Defect by Wavelet Packet Transformed Motor Current Signature Analysis , 2010, IEEE Transactions on Instrumentation and Measurement.

[3]  Yanhui Feng,et al.  Normalized wavelet packets quantifiers for condition monitoring , 2009 .

[4]  Beatrice Lazzerini,et al.  Robust Diagnosis of Rolling Element Bearings Based on Classification Techniques , 2013, IEEE Transactions on Industrial Informatics.

[5]  Hubert Razik,et al.  Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique , 2013, IEEE Transactions on Industrial Electronics.

[6]  Victor Songmene,et al.  The use of acoustic emission information to distinguish between dry and lubricated rolling element bearings in low-speed rotating machines , 2013 .

[7]  Izzet Yilmaz,et al.  Induction Motor Bearing Failure Detection and Diagnosis: Park and Concordia Transform Approaches Comparative Study , 2008 .

[8]  Zhi-Huan Song,et al.  A novel fault diagnosis system using pattern classification on kernel FDA subspace , 2011, Expert Syst. Appl..

[9]  Xiu Yao,et al.  Characteristic Study and Time-Domain Discrete- Wavelet-Transform Based Hybrid Detection of Series DC Arc Faults , 2014, IEEE Transactions on Power Electronics.

[10]  Wahyu Caesarendra,et al.  Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition , 2013 .

[11]  M. Elforjani and Condition Monitoring of Slow-Speed Shafts and Bearings with Acoustic Emission , 2010 .

[12]  José Antonio Lozano,et al.  Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Wensheng Su,et al.  Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement , 2010 .

[14]  François Guillet,et al.  A New Bearing Fault Detection Method in Induction Machines Based on Instantaneous Power Factor , 2008, IEEE Transactions on Industrial Electronics.

[15]  Peter W. Tse,et al.  Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition , 2012 .

[16]  Willey Yun Hsien Liew,et al.  An approach based on wavelet packet decomposition and Hilbert-Huang transform (WPD-HHT) for spindle bearings condition monitoring , 2012 .

[17]  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..

[18]  Mehmet Karaköse,et al.  A multi-objective artificial immune algorithm for parameter optimization in support vector machine , 2011, Appl. Soft Comput..

[19]  Nagarajan Murali,et al.  Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference , 2013, IEEE Transactions on Industrial Electronics.

[20]  Satish C. Sharma,et al.  Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform , 2013, Neurocomputing.

[21]  Giansalvo Cirrincione,et al.  Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks , 2013, IEEE Transactions on Industrial Electronics.

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

[23]  Murat Alper Basaran,et al.  Condition monitoring of speed controlled induction motors using wavelet packets and discriminant analysis , 2011, Expert Syst. Appl..

[24]  Peter W. Tse,et al.  A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals , 2013 .

[25]  Jokin Munoa,et al.  An integrated system for machine tool spindle head ball bearing fault detection and diagnosis , 2013, IEEE Instrumentation & Measurement Magazine.

[26]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[27]  J. Golinval,et al.  Fault detection based on Kernel Principal Component Analysis , 2010 .

[28]  Yaguo Lei,et al.  EEMD method and WNN for fault diagnosis of locomotive roller bearings , 2011, Expert Syst. Appl..

[29]  George Nikolakopoulos,et al.  Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines , 2013, Expert Syst. Appl..

[30]  B. Venkataramani,et al.  Evaluation of multiclass support vector machine classifiers using optimum threshold-based pruning technique , 2011 .

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

[32]  Jong-Duk Son,et al.  Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine , 2009, Expert Syst. Appl..

[33]  Tommy W. S. Chow,et al.  Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis , 2014, IEEE Transactions on Industrial Electronics.

[34]  Ivan Prebil,et al.  Multivariate and multiscale monitoring of large-size low-speed bearings using Ensemble Empirical Mode Decomposition method combined with Principal Component Analysis , 2010 .

[35]  Xiaoyuan Zhang,et al.  Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines , 2013 .

[36]  T.G. Habetler,et al.  Incipient Bearing Fault Detection via Motor Stator Current Noise Cancellation Using Wiener Filter , 2009, IEEE Transactions on Industry Applications.

[37]  Wei Zhou,et al.  Bearing Fault Detection Via Stator Current Noise Cancellation and Statistical Control , 2008, IEEE Transactions on Industrial Electronics.

[38]  B. Eftekharnejad,et al.  The application of spectral kurtosis on Acoustic Emission and vibrations from a defective bearing , 2011 .

[39]  Fulei Chu,et al.  Ensemble Empirical Mode Decomposition-Based Teager Energy Spectrum for Bearing Fault Diagnosis , 2013 .

[40]  Ivan Prebil,et al.  Non-linear multivariate and multiscale monitoring and signal denoising strategy using Kernel Principal Component Analysis combined with Ensemble Empirical Mode Decomposition method , 2011 .

[41]  Chrysostomos D. Stylios,et al.  Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition , 2013 .

[42]  Bo-Suk Yang,et al.  A Comparative Study on the Application of Acoustic Emission Technique and Acceleration Measurements for Low Speed Condition Monitoring , 2007 .

[43]  Li Jiang,et al.  Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis , 2013 .

[44]  Jianbo Yu,et al.  Local and Nonlocal Preserving Projection for Bearing Defect Classification and Performance Assessment , 2012, IEEE Transactions on Industrial Electronics.

[45]  Jie Liu,et al.  An Extended Wavelet Spectrum for Bearing Fault Diagnostics , 2008, IEEE Transactions on Instrumentation and Measurement.

[46]  Bo-Suk Yang,et al.  Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine , 2009 .

[47]  Andy C. C. Tan,et al.  Development of Testing Facilities for Verification of Machine Condition Monitoring Methods for Low Speed Machinery , 2006 .

[48]  Shaojiang Dong,et al.  Bearing degradation process prediction based on the PCA and optimized LS-SVM model , 2013 .

[49]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

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

[51]  Alberto Bellini,et al.  Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison , 2008, IEEE Transactions on Industry Applications.

[52]  P. K. Kankar,et al.  Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform , 2011 .

[53]  Ezio Bassi,et al.  Stator Current and Motor Efficiency as Indicators for Different Types of Bearing Faults in Induction Motors , 2010, IEEE Transactions on Industrial Electronics.

[54]  Aitor Arnaiz,et al.  Ball bearing damage detection using traditional signal processing algorithms , 2013, IEEE Instrumentation & Measurement Magazine.

[55]  J. Rafiee,et al.  Application of mother wavelet functions for automatic gear and bearing fault diagnosis , 2010, Expert Syst. Appl..

[56]  Ingo Steinwart,et al.  Mercer’s Theorem on General Domains: On the Interaction between Measures, Kernels, and RKHSs , 2012 .

[57]  Satish C. Sharma,et al.  Rolling element bearing fault diagnosis using wavelet transform , 2011, Neurocomputing.

[58]  Jinde Zheng,et al.  Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis , 2013 .

[59]  Bo Ma,et al.  A recognition and novelty detection approach based on Curvelet transform, nonlinear PCA and SVM with application to indicator diagram diagnosis , 2011, Expert Syst. Appl..

[60]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[61]  M. Elforjani,et al.  Natural mechanical degradation measurements in slow speed bearings , 2009 .

[62]  Andy C. C. Tan,et al.  Experimental Study on Condition Monitoring of Low Speed Bearings : Time Domain Analysis , 2007 .

[63]  I. R. Praveen Krishna,et al.  Empirical mode decomposition of acoustic signals for diagnosis of faults in gears and rolling element bearings , 2012 .

[64]  M. Elforjani,et al.  Accelerated natural fault diagnosis in slow speed bearings with Acoustic Emission , 2010 .

[65]  Bhim Singh,et al.  Investigation of Vibration Signatures for Multiple Fault Diagnosis in Variable Frequency Drives Using Complex Wavelets , 2014, IEEE Transactions on Power Electronics.

[66]  Michael J. Devaney,et al.  Adjustable Speed Drive Bearing Fault Detection via Wavelet Packet Decomposition , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[67]  M. Elforjani,et al.  Condition Monitoring of Slow‐Speed Shafts and Bearings with Acoustic Emission , 2011 .

[68]  Balbir S. Dhillon,et al.  Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network , 2012 .