Dominant feature identification for industrial fault detection and isolation applications

Expert Systems with Applications

Volumn 38, Issue 8, 2011, Pages 10676-10684

  Zhou, Jun Hong ^a,b   Pang, Chee Khiang ^c   Lewis, Frank L ^d   Zhong, Zhao Wei ^b  

^a SINGAPORE INSTITUTE OF MANUFACTURING TECHNOLOGY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^d UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

Author keywords

Least Square Error (LSE); Neural Network (NN); Principal Component Analysis (PCA); Principal Feature Analysis (PFA); Singular Value Decomposition (SVD)

Indexed keywords

COST SAVING; FAULT CLASSIFICATION; FAULT DETECTION AND ISOLATION; FAULT DIAGNOSIS; FAULT IDENTIFICATIONS; FEATURE IDENTIFICATION; FEATURE SETS; FEATURE SUBSET; INDUSTRIAL MACHINES; INTELLIGENT DIAGNOSIS; LEAST SQUARE ERRORS; MULTIPLE FAULTS; PRINCIPAL COMPONENT ANALYSIS (PCA); PRINCIPAL FEATURE ANALYSIS (PFA); PRODUCTION COST; SINGULAR VALUE DECOMPOSITION (SVD); TWO STAGE;

DAMAGE DETECTION; DIAGNOSIS; INDUSTRY; LEAST SQUARES APPROXIMATIONS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; QUALITY ASSURANCE; RELIABILITY; SECURITY OF DATA; SENSORS; SIGNAL PROCESSING; SINGULAR VALUE DECOMPOSITION;

FAULT DETECTION;

EID: 79953690789     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.01.160     Document Type: Article

References (28)

1. M.E.H. Benbouzid A review of induction motors signature analysis as a medium for faults detection IEEE Transactions on Industrial Electronics 47 5 2000 984 993
2. K.T. Chung, and A. Geddam A multi-sensor approach to the monitoring of end milling operations Journal of Materials Processing Technology 139 1-3 2003 15 20
3. Cohen, I.; Tian, Q.; Zhou, X.S.; & Huang, T.S. (2002). Feature selection using principal feature analysis. In Proc. of the 15th International Conference on Information Processing, Rochester, NY, USA, September 22-25.
4. Dang, P.; Ham, F.M.; Lewis, F.L.; & Stephanou, H. (2009). A Two-Stage Neural Network for Expression Classification, submitted to Neurocomputing, April.
5. C.W. De Silva Vibration: fundamentals and practice 2nd ed. 2006 CRC Press
6. F.M. Ham, and I. Kostanic Principles of Neurocomputing for Science and Engineering 2001 McGraw Hill New York
7. T. Han, and B.-S. Yang Development of an E-maintenance system integrating advanced techniques Computers in Industry 57 6 2006 569 580 (Pubitemid 44109902)
8. Available from: .
9. Available from: .
10. I.T. Jolliffe Principal Component Analysis 1986 Springer-Verlag New York
11. Ko, M.; & Lee, J. (2001). A system framework for next-generation E-maintenance system. In Proc. of Second International Symposium on Environmentally Conscious Design and Inverse Manufacturing, Tokyo, Japan.
12. Y. Lei, Z. He, and Y. Zi Application of an intelligent classification method to mechanical fault diagnosis Expert Systems with Applications 36 6 2009 9941 9948
13. Y. Lei, M.J. Zuo, Z. He, and Y. Zi A multidimensional hybrid intelligent method for gear fault diagnosis Expert Systems with Applications 37 2 2010 1419 1430
14. F.L. Lewis, S. Jagannathan, and A. Yeildirek Neural Network Control of Robot Manipulators and Nonlinear Systems 1998 CRC Press
15. T.I. Liu, E.J. Ko, and S.L. Sha Intelligent monitoring of tapping tools Journal of Materials Shaping Technology 8 4 1990 249 254
16. Lu, Y.; Cohen, I.; Zhou, X.S.; Tian, Q.; & Huang, T.S. (2007). Feature Selection Using Principal Feature Analysis. In Proc. of the 15th International Conference on Multimedia, Augsburg, Germany, September 25-29, pp. 301-304.
17. A.C. Marquez, and J.M.D. Gupta Contemporary maintenance management: process, framework and supporting pillars Omega 34 3 2006 313 326
18. P.D. McFadden, and J.D. Smith Vibration monitoring of rolling element bearings by the high frequency resonance technique-a review Tribology International 17 1984 3 10 (Pubitemid 14561780)
19. A. Muller, A.C. Marquez, and B. Lung On the concept of E-maintenance: review and current research Reliability Engineering and System Safety 93 8 2008 1165 1187 (Pubitemid 351418143)
20. Norton, M.; & Karczub, D. (2003). Fundamentals of Noise and Vibration Analysis for Engineers (2nd ed.). Cambridge.
21. Qiu, H.; Luo, H.; & Eklund, N. (2009). Draft: on-board bearing prognostics in aircraft engine: enveloping analysis or FFT?. In Proc. Of the IDETC/CIE, San Diego, CA, USA, August 30-September 2, pp. 1-7.
22. N. Tandon, and A. Choudhury A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings Tribology International 32 8 1999 469 480 (Pubitemid 30550533)
23. Tao, B.; Ding, H.; & Xiong, Y.L. (2003). IP sensor and its distributed networking application in E-maintenance. In Proc. of the 2003 IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, Washington, DC, USA, October 5-8, pp. 3858-3863.
24. A. Widodo, B.-S. Yang, D.-S. Gu, and B.-K. Cho Intelligent fault diagnosis system of induction motor based on transient current signal Mechatronics 19 5 2009 680 689
25. Zhang, W.; Halang, W.; & Diedrich, C. (2003). An agent-based platform for service integration in E-maintenance. In Proc of the 2003 IEEE International Conference on Industrial Technology, Vol. 1, No. 10-12, pp. 426-433, December. (Pubitemid 40761478)
26. Zhou, W.; Habetler, T.G.; & Harley, R.G. (2007). Bearing condition monitoring methods for electric machines: a general review. In Proc. Of the IEEE International Symposium on Diagnostics for Electric Machine, Cracow, Poland, September 6-8.; pp. 3-6.
27. J.-H. Zhou, C.K. Pang, F.L. Lewis, and Z.-W. Zhong Intelligent diagnosis and prognosis of tool wear using dominant feature identification IEEE Transactions on Industrial Informatics 5 4 2009 454 464
28. J.-H. Zhou, C.K. Pang, Z.-W. Zhong, and F.L. Lewis Tool wear monitoring using acoustic emissions by dominant feature identification IEEE Transactions on Instrumentation and Measurement 60 2 2011 547 559