Abstract
In rotating machinery one of the prominent causes of malfunction is faults generated in ball bearings, therefore, diagnosis and interpretation of these faults is essential before they become severe. Feature extraction methodology has been presented in this paper based on application of lifting wavelet transform. Minimum permutation entropy is considered as decision making for selecting level of lifting wavelet transform. Sixteen features are calculated from measured vibration signals for various bearing conditions like defect in inner race, outer race, ball defect, combined defect and no defect condition. To achieve better fault identification accuracy selection of features carrying useful information is needed. To select highly distinguished features various ranking methodologies such as Fisher score, ReliefF, Wilcoxon rank, Gain ratio and Memetic feature selection are used. The ranked feature sets that are fed to machine learning algorithms support vector machine, learning vector quantization and artificial neural network for identification of bearing conditions. Tenfold cross-validation results show that selected features give enhanced accuracy for detecting faults. Features selected through Fisher score-support vector machine and ReliefF-artificial neural network gives 100 % cross-validation accuracy. Result shows that proposed methodology is feasible and effective for fault diagnosis of bearing with reduced feature set.
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Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88:174102
Claypoole, RL, Baraniuk RG, Nowak RD (1998) Adaptive wavelet transforms via lifting. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, vol 3, pp 1513–1516. doi:10.1109/ICASSP.1998.681737
Daubechies I, Sweldens W (1998) Factoring wavelet transforms into lifting steps. J Fourier Anal 4:247–269
Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York
Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289–1305
Geronimo JS, Hardin DP, Massopust PR (1994) Fractal functions and wavelet expansions based on several scaling functions. Approx Theory 78(3):373–401
Hall M, Holmes G (2003) Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans Knowl Data Eng 15:1–16
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10–18
Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press Cambridge, Massachusetts
Heng RBW, Nor MJM (1998) Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition. Appl Acoust 53:211–226
He X, Cai D, Niyogi P (2005) Laplacian score for feature selection. In: Advances in neural information processing Systems, vol 18, Cambridge, MA
Hively LM, Protopopescu VA (2004) Machine failure for warning via phase-space dissimilarity measures. Chaos 14:408–419
Janjarasjitt S, Ocak H, Loparo KA (2008) Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal. J Sound Vib 317:112–126
Kankar PK, Sharma SC, Harsha SP (2011a) Rolling element bearing fault diagnosis using wavelet transform. Neurocomputing 74:1638–1645
Kankar PK, Sharma SC, Harsha SP (2011b) Fault diagnosis of ball bearings using continuous wavelet transform. Appl Soft Comput 11:2300–2312
Kankar PK (2011c) Fault diagnosis of rolling element bearings using vibration signature analysis. Ph.D. Dissertation, IIT Roorkee
Kappaganthu K, Nataraj C (2011) Feature selection for fault detection in rolling element bearings using mutual information. J Vib Acoust 133:1–12
Kohonen T (1990) The self-organizing map. In: Proceedings of the IEEE, pp 1464–1480
Lei YG, Zuo MJ, He ZJ, Zi YY (2010) A multidimensional hybrid intelligent method for gear fault diagnosis. Expert Syst Appl 37:1419–1430
Liu H, Setiono R (1995) Feature selection and discretization of numeric attributes. In: Proceedings of the seventh IEEE international conference on tools with artificial intelligence, November 5–8, pp 388–391, Herndon, Virginia
Martin HR, Honarvar F (1995) Application of statistical moments to bearing failure detection. Appl Acoust 44:67–77
Ooi CH, Chetty M, Teng SW (2006) Differential prioritization between relevance and redundancy in correlation-based feature selection techniques for multiclass gene expression data. BMC Bioinform 7:320
Pandya DH, Upadhyay SH, Harsha SP (2014) Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft Comput 18:255–266
Samanta B, Al-Balushi KR, Al-Araimi SA (2006) Artificial neural networks and genetic algorithm for bearing fault detection. Soft Comput 10:264–271
Samuel PD, Pines DJ (2005) A review of vibration-based techniques for helicopter transmission diagnostics. J Sound Vib 282:475–508
Sharma A, Dey S (2012) Performance investigation of feature selection methods and sentiment lexicons for sentiment analysis. Special Issue of International Journal of Computer Applications (0975-8887) on Advanced Computing and Communication Technologies for HPC Applications, pp 15–20
Sikonja MR, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn J 53:23–69
Sweldens W (1997) The lifting scheme: a construction of second generation wavelet. SIAM J Math Anal 29:511–546
Swets DL, Weng JJ (1995) Efficient content-based image retrieval using automatic feature selection. In IEEE international symposium on computer vision, pp 85–90
Üstün B, Melssen WJ, Buydens LMC (2006) Facilitating the application of support vector regression by using a universal Pearson VII function based kernel. Chemom Intell Lab Syst 81(1):29–40
Vakharia V, Gupta VK, Kankar PK (2014) A multiscale entropy based approach to select wavelet for fault diagnosis of ball bearings. J Vib Control. doi:10.1177/1077546314520830
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Wen XB, Zhang H, Xu XQ, Quan JJ (2006) A new watermarking approach based on probabilistic neural network in wavelet domain. Soft Comput 13(4):355–360
Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80–83
Wu SD, Wu PH, Wu CW, Ding JJ, Wang CC (2012) Bearing fault diagnosis based on multiscale permutation entropy and support vector machine. Entropy 14:1343–1356
Yan R, Gao RX (2007) Approximate entropy as a diagnosis tool for machine health monitoring. Mech Syst Signal Process 21:824–839
Yang Z, Cai L, Gao L, Wang H (2012) Adaptive redundant lifting wavelet transform based on fitting for fault feature extraction of roller bearings. Sensors 12:4381–4398
Zanin M, Luciano Z, Osvaldo AR, David P (2012) Permutation entropy and its main biomedical and econophysics applications : a review. Entropy 14:1553–1577
Zhao Z, Morstatter F, Sharma S, Alelyani S, An A, Liu H (2010) Advancing feature selection research-ASU feature selection repository. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe
Xiong N, Funk P (2010a) Construction of fuzzy knowledge bases incorporating feature selection. Soft Comput 10(9):796–804
Xiong N, Funk P (2010b) Combined feature selection and similarity modeling in case-based reasoning using hierarchical Memetic algorithm. In: Proceedings of the IEEE World Congress on Computational Intelligence, pp 1537–1542
Acknowledgments
The authors would like to thank Prof. Satish C. Sharma and Dr. S.P. Harsha, Vibration and Noise Control Laboratory, Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, India for their support to carry out this study.
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Communicated by V. Loia.
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Vakharia, V., Gupta, V.K. & Kankar, P.K. A comparison of feature ranking techniques for fault diagnosis of ball bearing. Soft Comput 20, 1601–1619 (2016). https://doi.org/10.1007/s00500-015-1608-6
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DOI: https://doi.org/10.1007/s00500-015-1608-6