International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.11, No.5, May. 2019

Bearing Fault Detection Using Logarithmic Wavelet Packet Transform and Support Vector Machine

Full Text (PDF, 987KB), PP.21-33

Views:9   Downloads:0


Om Prakash Yadav, G L Pahuja

Index Terms

Fisher’s ranking method;inner raceway defect;ball bearing defect;kernel principal component analysis;support vector machine;wavelet packet decomposition


Objective: This paper presents an automated approach that combines Fisher ranking and dimensional reduction method as kernel principal component analysis (KPCA) with support vector machine (SVM) to accurately classify the defects of rolling element bearing used in induction motor.
Methodology: In this perspective, vibration signal produced by rolling element bearing was decomposed to four levels using wavelet packet decomposition (WPD) method. Thirty one Logarithmic Root Mean Square Features (LRMSF) were extracted from four level decomposed vibration signals. Initially, thirty one features were rank by Fisher score and top ten rank features were selected. For effective detection, top ten features were reduced to a new feature using dimension reduction methods as KPCA and generalized discriminant analysis (GDA). After this, the new feature applied to SVM for binary classification of bearing defects. For analysis of this thirty six standard vibration datasets taken from online available bearing data center website of Case Western Reserve University on bearing conditions like healthy (NF), inner race defect (IR) and ball bearing (BB) defects at different loads. 
Results: The simulated numerical results show that proposed method KPCA with SVM classifier using Gaussian Kernel achieved an accuracy (AC) of 100, Sensitivity (SE) of 100%, Specificity (SP) of 99.3% and Positive prediction value (PPV) of 99.3% for NF_IRB dataset, and an AC of 100, SE of 99.8%, SP of 100% and PPV of 100% for NF_BBB dataset.

Cite This Paper

Om Prakash Yadav, G L Pahuja, "Bearing Fault Detection Using Logarithmic Wavelet Packet Transform and Support Vector Machine", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.5, pp. 21-33, 2019.DOI: 10.5815/ijigsp.2019.05.03


[1]S. Nandi, H. A. Toliyat, and X. Li, “Condition monitoring and fault diagnosis of electrical motors - A review,” IEEE Transactions on Energy Conversion, vol. 20, no. 4. pp. 719–729, 2005.

[2]N. Tandon, G. S. Yadava, and K. M. Ramakrishna, “A comparison of some condition monitoring techniques for the detection of defect in induction motor ball bearings,” Mech. Syst. Signal Process., vol. 21, no. 1, pp. 244–256, 2007.

[3]P. Zhang, Y. Du, T. G. Habetler, and B. Lu, “A survey of condition monitoring and protection methods for medium-voltage induction motors,” IEEE Transactions on Industry Applications, vol. 47, no. 1. pp. 34–46, 2011.

[4]M. Blodt, P. Granjon, B. Raison, and G. Rostaing, “Models for bearing damage detection in induction motors using stator current monitoring,” IEEE Trans. Ind. Electron., vol. 55, no. 4, pp. 1813–1822, 2008.

[5]F. Immovilli, A. Bellini, R. Rubini, and C. Tassoni, “Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison,” IEEE Trans. Ind. Appl., vol. 46, no. 4, pp. 1350–1359, 2010.

[6]C. Ruiz-Cárcel, V. H. Jaramillo, D. Mba, J. R. Ottewill, and Y. Cao, “Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions,” Mech. Syst. Signal Process., 2016.

[7]H. Qiu, J. Lee, J. Lin, and G. Yu, “Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics,” J. Sound Vib., 2006.

[8]O. Rioul and M. Vetterli, “Wavelets and Signal Processing,” IEEE Signal Process. Mag., 1991.

[9]S. Abbasion, A. Rafsanjani, A. Farshidianfar, and N. Irani, “Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine,” Mech. Syst. Signal Process., vol. 21, no. 7, pp. 2933–2945, 2007.

[10]P. Konar and P. Chattopadhyay, “Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs),” Appl. Soft Comput., vol. 11, no. 6, pp. 4203–4211, 2011.

[11]H. Erişti, A. Uçar, and Y. Demir, “Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines,” Electr. Power Syst. Res., vol. 80, no. 7, pp. 743–752, 2010.

[12]Y. Wang, G. Xu, L. Liang, and K. Jiang, “Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis,” Mech. Syst. Signal Process., 2015.

[13]Z. Zhang, Y. Wang, and K. Wang, “Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network,” J. Intell. Manuf., 2013.

[14]J.-D. Wu and C.-H. Liu, “An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network,” Expert Syst. Appl., 2009.

[15]A. Tabrizi, L. Garibaldi, A. Fasana, and S. Marchesiello, “Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine,” Meccanica, 2015.

[16]L. Y. Zhao, L. Wang, and R. Q. Yan, “Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy,” Entropy, 2015.

[17]S. De Wu, P. H. Wu, C. W. Wu, J. J. Ding, and C. C. Wang, “Bearing fault diagnosis based on multiscale permutation entropy and support vector machine,” Entropy, 2012.

[18]Daisuke Matsuoka, “Extraction, classification and visualization of 3-dimensional clouds simulated by cloud-resolving atmospheric model,” Int. J. Model. Simulation, Sci. Comput., vol. 8, no. 4, pp. 1–15.

[19]T. W. Rauber, F. De Assis Boldt, and F. M. Varejão, “Heterogeneous feature models and feature selection applied to bearing fault diagnosis,” IEEE Trans. Ind. Electron., vol. 62, no. 1, pp. 637–646, 2015.

[20]B. R. Nayana and P. Geethanjali, “Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults from Vibration Signal,” IEEE Sens. J., vol. 17, no. 17, pp. 5618–5625, 2017.

[21]J. G. Dy and C. E. Brodley, “Feature Selection for Unsupervised Learning ,” J. Mach. Learn. Res., 2004.

[22]J. Tang, S. Alelyani, and H. Liu, “Feature Selection for Classification: A Review,” Data Classif. Algorithms Appl., 2014.

[23]C. Rajeswari, B. Sathiyabhama, S. Devendiran, and K. Manivannan, “Bearing fault diagnosis using multiclass support vector machine with efficient feature selection methods,” Int. J. Mech. Mechatronics Eng., 2015.

[24]C. Wang, L. M. Jia, and X. F. Li, Fault diagnosis method for the train axle box bearing based on KPCA and GA-SVM. 2014.

[25]F. Deng, S. Yang, Y. Liu, Y. Liao, and B. Ren, “Fault Diagnosis of Rolling Bearing Using the Hermitian Wavelet Analysis, KPCA and SVM,” in Proceedings - 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2017, 2017.

[26]“Case Western Reserve University Bearing Data Center.” [Online]. Available:

[27]R. N. Khushaba, S. Kodagoda, S. Lal, and G. Dissanayake, “Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm,” IEEE Trans. Biomed. Eng., 2011.

[28]O. Aran and L. Akarun, “A multi-class classification strategy for Fisher scores: Application to signer independent sign language recognition,” Pattern Recognit., 2010.

[29]V. T. Tran, F. AlThobiani, A. Ball, and B. K. Choi, “An application to transient current signal based induction motor fault diagnosis of Fourier-Bessel expansion and simplified fuzzy ARTMAP,” Expert Syst. Appl., 2013. 

[30]B. M. Asl, S. K. Setarehdan, and M. Mohebbi, “Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal,” Artif. Intell. Med., 2008.

[31]B. Scholkopf, A. J. Smola, K. R. Muller, M. Kybernetik, B. Schlkopf, and K. R. Müller, “Kernel principal component analysis,” Adv. kernel methods Support vector Learn., 1999.

[32]S. Dong et al., “Bearing degradation state recognition based on kernel PCA and wavelet kernel SVM,” Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., 2015.

[33]X. Jin, L. Lin, S. Zhong, and G. Ding, “Rotor fault analysis of classification accuracy optimition base on kernel principal component analysis and SVM,” in Procedia Engineering, 2011.

[34]V. N. Vapnik, “The Nature of Statistical Learning Theory,” Springer. 1995.

[35]O. P. Yadav, D. Joshi, and G. L. Pahuja, “Support Vector Machine based Bearing Fault Detection of Induction Motor,” Indian J. Adv. Electron. Eng., vol. 1, no. 1, pp. 34–39, 2013.

[36]B. Zhou and J. Xu, “An adaptive SVM-based real-time scheduling mechanism and simulation for multiple-load carriers in automobile assembly lines,” Int. J. Model. Simulation, Sci. Comput., 2017.

[37]D. M. J. Tax and R. P. W. Duin, “Support Vector Data Description,” J. Dyn. Syst. Meas. Control, 2004.

[38]J. Altmann and J. Mathew, “Multiple band-pass autoregressive demodulation for rolling-element bearing fault diagnosis,” Mech. Syst. Signal Process., 2001.