Fault diagnosis for bearing based on Mahalanobis-Taguchi system

Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012

Volumn , Issue , 2012, Pages

  Wang, Zhipeng ^a   Wang, Zili ^a   Tao, Laifa ^a   Ma, Jian ^a  

^a BEIHANG UNIVERSITY   (China)

Author keywords

Bearing; Fault Diagnosis; Mahalanobis distance; Mahalanobis Taguchi system (MTS)

Indexed keywords

COMPUTATIONAL SCHEMES; DIAGNOSIS METHODS; DOMAIN ANALYSIS; MAHALANOBIS DISTANCES; MAHALANOBIS-TAGUCHI SYSTEMS; MULTI VARIATE ANALYSIS; PRINCIPAL COMPONENTS; ROLLING ELEMENT BEARING; VIBRATION DATA;

BEARINGS (STRUCTURAL); FAILURE ANALYSIS; FEATURE EXTRACTION; TAGUCHI METHODS;

SYSTEMS ENGINEERING;

EID: 84866145944     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PHM.2012.6228834     Document Type: Conference Paper

References (15)

1. P. M. Frank, Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy - a survey and some new results, Automatica 26(1990) 459-474.
2. J.J. Gertler, Survey of model-based failure detection and isolation in complex plants, IEEE Control Systems Magazine 8 (1988) 3-11.
3. T. Boutros, M. Liang, Detection and diagnosis of bearing and cutting tool faults using hidden Markov models, Mechanical Systems and Signal Processing 25 (2011) 2102-2124.
4. H. Ocak, and K. A. Loparo, "A New Bearing Fault Detection and Diagnosis Scheme Based on Hidden Markov Modeling of Vibration Signals,"Acoustics, Speech, and Signal Processing, ICASSP Proceedings, Vol. 5, 2001, pp.3141-3144.
5. S. Janjarasjitt, H. Ocak, and K. A. Loparo, "Bearing Condition Diagnosis and Prognosis Using Applied Nonlinear Dynamical Analysis of Machine Vibration Signal," J. Sound Vib., 3171-2, 2008, pp. 112-126.
6. R. Yan, and R. X. Gao, "An Efficient Approach to Machine Health Diagnosis Based on Harmonic Wavelet Packet Transform," Robot. Comput. Integr. Manufact., 214-5, 2005, pp. 291-301.
7. P. W. Tse, Y. H. Peng, and R. Yam, "Wavelet Analysis and Envelope Detection for Rolling Element Bearing Fault Diagnosis - Their Effectiveness and Flexibilities," ASME J. Vibr. Acoust., 1233, 2001, pp. 303-310.
8. X. Lou, and K. A. Loparo, "Bearing Fault Diagnosis Based on Wavelet Transform and Fuzzy Inference," Mech. Syst. Signal Process., 185, 2004, pp.1077-1095.
9. S. Kumar, V. Sotiris, and M. Pecht, Health assessment of electronic products using Mahalanobis distance and projection pursuit analysis, World Academy of Science, Engineering and Technology 38, 2008, pp. 659-667.
10. S. Kumar, T.W.S. Chow, and M. Pecht, Approach to fault identification for electronic products using Mahalanobis distance, 59(8), 2010, pp 2055-2064.
11. H. R. Cao, X.F. Chen, Y. Y. Zi ; F. Ding, H. X. Chen, J.Y. Tan, Z.J. He, End milling tool breakage detection using lifting scheme and Mahalanobis distance, International Journal of Machine Tools & Manufacture, 48(2), 2008, pp141-151.
12. A. Soylemezoglu, S. Jagannathan, C. Saygin, Mahalanobis Taguchi System (MTS) as a Prognostics Tool for Rolling Element Bearing Failures, Journal of Manufacturing Science and Engineering (Transactions of the ASME), 132(5), 2010, 051014 (12 pp).
13. G. Taguchi, S. Chowdury, and Y. Wu, The Mahalanobis Taguchi System, McGraw-Hill, New York, 2001.
14. P. C. Mahalanobis,"On the Generalized Distance in Statistics," Proceedings, National Institute of Science of India,Vol.21, 1936, pp. 49-55.
15. G. Taguchi, and R. Jugulum, The Mahalanobis-Taguchi Strategy - A Pattern Technology System, Wiley, New York, 2002.