Feature extraction and fault severity classification in ball bearings

JVC/Journal of Vibration and Control

Volumn 22, Issue 1, 2016, Pages 176-192

  Sharma, Aditya ^a   Amarnath, M ^a   Kankar, P K ^a  

^a PDPM INDIAN INSTITUTE OF INFORMATION TECHNOLOGY   (India)

Author keywords

artificial neural network; bearing; entropy; Fault severity classification; support vector machine

Indexed keywords

ARTIFICIAL INTELLIGENCE; BEARINGS (STRUCTURAL); ENTROPY; EXTRACTION; FEATURE EXTRACTION; HIGHER ORDER STATISTICS; INTERACTIVE COMPUTER SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; REAL TIME SYSTEMS; STATISTICAL METHODS; SUPPORT VECTOR MACHINES;

CLASSIFICATION EFFICIENCY; COMPARATIVE STUDIES; FAULT SEVERITIES; MACHINE LEARNING TECHNIQUES; STANDARD DEVIATION; STATISTICAL FEATURES; UNCERTAINTY MEASURES; VIBRATION SIGNAL;

BALL BEARINGS;

EID: 84952882177     PISSN: 10775463     EISSN: 17412986     Source Type: Journal    
DOI: 10.1177/1077546314528021     Document Type: Article

References (32)

1. Abbasion S, Rafsanjani A, Farshidianfar A, Irani N, (2007) Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mechanical Systems and Signal Processing 21: 2933-2945.
2. Arslan H, Aktürk N, (2008) An investigation of rolling element vibrations caused by local defects. ASME Journal of Tribology 130: 041101-12.
3. Chen J, Zia Y, He Z, Yuan J, (2013) Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet. Mechanical Systems and Signal Processing 38: 36-54.
4. Ertunc HM, Ocak H, Aliustaoglu C, (2013) ANN- and ANFIS-based multi-staged decision algorithm for the detection and diagnosis of bearing faults. Neural Comput & Applic 22 (Suppl 1): S435-S446.
5. Ghafari SH, Golnaraghi F, Ismail F, (2008) Effect of localized faults on chaotic vibration of rolling element bearings. Nonlinear Dyn 53: 287-301.
6. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH, (2009) The WEKA data mining software: an update. SIGKDD Explorations 11: 10-18.
7. Han J, Dong F and Xu YY (2009) Entropy feature extraction on flow pattern of gas/liquid two-phase flow based on cross-section measurement. In: Proceedings of The 6th International Symposium on Measurement Techniques for Multiphase Flows, Naha, Okinawa, Japan, Journal of Physics: Conference Series, 147: 012041.
8. Hong H, Liang M, (2009) Fault severity assessment for rolling element bearings using the Lempel-Ziv complexity and continuous wavelet transform. Journal of Sound and Vibration 320: 452-468.
9. Jiang F, Li W, Wang Z, Zhu Z, (2012) Fault severity estimation of rotating machinery based on residual signals. Advances in Mechanical Engineering 2012: 1-8.
10. Kankar PK, Sharma SC, Harsha SP, (2011a) Fault diagnosis of ball bearings using machine learning methods. Expert Systems with Applications 38: 1876-1886.
11. Kankar PK, Sharma SC, Harsha SP, (2011b) Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform. Journal of Vibration and Control 17: 2081-2094.
12. Kankar PK, Sharma SC, Harsha SP, (2013) Vibration signature analysis of a high speed rotor supported on ball bearings due to localized defects. Journal of Vibration and Control 19: 1833-1853.
13. Li R, Sopon P, He D, (2012) Fault features extraction for bearing prognostics. J Intell Manuf 23: 313-321.
14. Liu Z, Cao H, Chen X, He Z, Shen Z, (2013) Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing 99: 399-410.
15. Loparo KA (2013) Bearing vibration data set: Case western reserve university bearing data centre website. Available at www.csegroups.case.edu/bearingdatacenter/home (accessed in May 2013).
16. Meyer D, Leisch F, Hornik K, (2003) The support vector machine under test. Neurocomputing 55: 169-186.
17. Moshou D, Kateris D, Sawalhi N, Loutridis S and Gravalos I (2010) Fault severity estimation in rotating mechanical systems using feature based fusion and self-organizing maps. In: Proceedings of Artificial Neural Networks-ICANN 20th International Conference, Thessaloniki, Greece, 15-18 September 2010, pp. 410-413.
18. Platt JC (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Microsoft Research, Technical Report, MSR-TR-98-14: 1-21.
19. Tiwari R, Gupta VK, Kankar PK, (2013) Bearing fault diagnosis based on multi-scale permutation entropy and adaptive neuro fuzzy classifier. Journal of Vibration and Control. Epub ahead of print 8 July 2013. DOI: 10.1177/1077546313490778.
20. Samanta B, (2004) Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Processing 18: 625-644.
21. Samanta B, Al-Balushi KR, (2003) Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Expert Systems with Applications 17: 317-328.
22. Samanta B, Al-Balushi KR, Al-Araimi SA, (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence 16: 657-665.
23. Saxena A, Saad A, (2007) Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems. Applied Soft Computing 7: 441-454.
24. Shen Z, He Z, Chen X, Sun C, Liu Z, (2012) A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time. Sensors 12: 10109-10135.
25. Sugumaran V, Muralidharan V, Ramachandran KI, (2007) Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mechanical Systems and Signal Processing 21: 930-942.
26. Üstün B, Melssen WJ, Buydens LMC, (2006) Facilitating the application of support vector regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems 81: 29-40.
27. Wang J, Cui L, Wang H, Chen P, (2013) Improved complexity based on time-frequency analysis in bearing quantitative diagnosis. Advances in Mechanical Engineering 2013: 1-11.
28. Wang W, Ismail F, Golnaraghi F, (2004) Aneuro-fuzzy approach to gear system monitoring. IEEE Transactions on Fuzzy Systems 12: 710-723.
29. Widodo A, Yang B-S, (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing 21: 2560-2574.
30. Witten IH, Frank E, (2005) Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. San Francisco: Morgan Kaufmann.
31. Wu S-D, Wu P-H, Wu C-W, Ding J-J, Wang C-C, (2012) Bearing fault diagnosis based on multiscale permutation entropy and support vector machine. Entropy 14: 1343-1356.
32. Xian G-M, Zeng B-Q, (2009) An intelligent fault diagnosis method based on wavelet packet analysis and hybrid support vector machines. Expert Systems with Applications 36: 12131-12136.