Mechanical fault diagnosis using wireless sensor networks and a two-stage neural network classifier

IEEE Aerospace Conference Proceedings

Volumn , Issue , 2009, Pages

  Ballal, Prasanna ^a   Ramani, A ^a   Middleton, M ^a   McMurrough, C ^a   Athamneh, A ^a   Lee, W ^a   Kwan, C ^b   Lewis, F ^a  

^a UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

^b Signal Processing Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BEARING FAULT; CLASSIFICATION METHODS; FAULT FEATURE; FAULT MODES; GENERALIZED HEBBIAN ALGORITHM; LOW-COST TEST; MECHANICAL FAILURES; MECHANICAL FAULT DIAGNOSIS; MOTOR FAULT; NEURAL NETWORK CLASSIFIER; PRINCIPAL COMPONENTS; TWO STAGE; VIBRATION DATA; VIBRATION SIGNATURE; WIRED SENSORS; WIRELESS SENSOR;

AEROSPACE APPLICATIONS; BEARINGS (MACHINE PARTS); BEARINGS (STRUCTURAL); FAILURE ANALYSIS; FEATURE EXTRACTION; MOTORS; PRINCIPAL COMPONENT ANALYSIS; QUALITY ASSURANCE; REMOTE SENSING; SELF ORGANIZING MAPS; TEST FACILITIES; VECTOR QUANTIZATION;

WIRELESS SENSOR NETWORKS;

EID: 70349090590     PISSN: 1095323X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/AERO.2009.4839671     Document Type: Conference Paper

References (45)

1. Barron R.," Engineering Condition Monitoring: Practice, Methods and Applications", Langman Ltd., 1996
2. Bishop C.M, "Neural Networks for Pattern Recognition", Oxford University Press Inc., New York, 1995
3. Chen, C., "Signal and image processing for remote sensing" CRC Press Technology & Industrial Arts 648 pages ISBN 0849350913, 2006
4. Chitroub S., Houacine A., and Sansal B., "Principal Component Analysis of Multispectral Images Using Neural Networks, "Computer Systems and Applications, ACS/IEEE International, Page(s):89 - 95
5. Dang, Pritpal S, "A potential field approach for distributed control of discrete actuator and sensor arrays, Ph.D., University of Texas at Arlington, 2007, AAT 3288909, Page(s)
6. Daniel Herbert Hanson, "Artificial Neural Network Based Survival Analysis for Hydro-Mechanical Systems", Ph.D., The University of Iowa, 2004
7. Davies A.," Handbook of Condition Monitoring: Techniques and Methodology" London: Chapman & Hall, 1998
8. Erbay, Ali Seyfettin ,"Multisensor fusion for induction motor aging analysis and fault diagnosis", Ph.D., The University of Tennessee, 1999, 363 pages
9. th Annual Conference of the IEEE Industrial Electronics Society, Vol. 4, pp. 1961-1965
10. Gorrell G., "Generalized hebbian algorithm for incremental singular value decomposition in natural language processing", In EACL, 2006.
11. Hasan, Ocak, "Fault detection, diagnosis and prognosis of rolling element bearings: Frequency domain methods and hidden Markov modeling", Ph.D., Case Western Reserve University, 2004
12. Ham Fredric M., Kostanic Ivica," Principles of Neurocomputing for Science and Engineering", McGraw-Hill, Inc ISBN 0070259666
13. Harris, T.A., Rolling bearing analysis, John Wiley, 2001.
14. Haykin Simon, "Neural Networks, A Comprehensive Foundation", Pearson Education ISBN 81-7808-300-0
15. Heng, R.B.W. , Nor, M.J.M. ," Statistical Analysis of Sound and Vibration Signals for Monitoring Rolling Element Bearing Condition", Applied Acoustics, Vol. 53, pp. 211-226, 1998
16. Howard, I., "A Review of Rolling Element Bearing Vibration: Detection, Diagnosis and Prognosis", Aeronautical and Maritime Research laboratory Airframes and Engineer Division, 1994
17. Hsieh William W., "Nonlinear Principal Component Analysis Using Auto Associative Neural Networks", AIChE Journal [0001-1541., KRAMER yr:1991 vol:37 iss:2 pg:233
18. Koosial, Jainarine, "Neural Network Based classification of bearing faults in rotating machines: Augmentation of vibration measurements with power measurements", M.Sc.Eng., Queen's University at Kingston (Canada), 2006, 121 pages; AAT MR18753
19. Kwan C., X.Zhang, R Xu, and L.Haynes, "A Novel Approach to Fault Diagnostics and Prognostics," Int. Conference on robotics and Automation, May, 2003.
20. Lai, Ming Sang ,"Detection of developing bearing failures by means of vibration analysis", Ph.D., University of Windsor (Canada), 1990,242 pages; AAT NN65146
21. Li Bo, Gregory Goddu, Mo-Yuen Chow, "Detection of Common Motor Bearing Faults Using Frequency Domain Vibration Signals and a Neural Network Based Approach", Proceedings of the American Control Conference, Philadelphia, Pennsylvania, June 1998
22. Li, C.J., Ma, J., "Bearing Localization Defect Detection by Bicoherence Analysis of Vibrations", Journal of Engineering for Industry, Journal of Engineering for Industry, Vol. 117, pp. 625-629, 1995
23. Li, C.J. , Ma, J., "Wavelet Decomposition of Vibrations for Detection of Bearing Localized Faults", NDT&E International, Vol. 30, pp.143-149
24. Lindsay I Smith, "A tutorial on Principal Component Analysis"
25. Li, Lingxin, "Variable Speed AC Induction motor fault detection and diagnosis using artificial neural networks", M.Sc.(Eng), Queen's University at Kingston (Canada), 2005, 111 pages; AAT MQ99911
26. Martin T., Hagan, Howard Demuth. B, Beale Mark, "Neural Network Design", ' ISBN 0-534-94332-2
27. Martin, H.R. , Honarvar F., "Application of Statistical Moments to Bearing Failure Detection", Applied Acoustics, Vol. 44, pp.67-77, 1995
28. McCormick, A.C., Nandi, A.K., "Bispectral and Trispectral Features for Machine Condition Diagnosis ", IEEE Proc. -Vis. Image Process, Vol. 146, No. 5, pp. 229-234, October 1999.
29. McFadden, P.D. , Smith J.D. , "Model for the Vibration Produced by a Single Point Defect in a Rolling Element Bearing", Journal of Sound and Vibration, 96(1), pp-69-82, 1984
30. McFadden, P.D. , Smith J.D. , "Vibration Monitoring of Rolling Element Bearings by High Frquency Resonance Technique- A Review", Tribology International, Vol. 77, pp. 3-10
31. McInerny S. and Dai Y., Basic vibration signal processing for bearing fault detection. Education, IEEE Transactions on, Volume 46, Issue1, Feb. 2003 Page(s):149- 156
32. Ocak, H. , Loparo, K.A. , "A New Bearing Fault Detection and Diagnosis Scheme based on Hidden Markov Modeling of Vibration Signals", Proceedings if IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. 5, pp. 3141-3144, May2001
33. Pansini Antony J., Basics of Electric Motors, PennWell Publishing Company, 1996.
34. Rao B. K. N., " Handbook of Condition Monitoring" Amsterdam: Elsevier Advanced Technology, 1996
35. Rezaei, Aida "Fault Detection and Diagnosis on the rolling element bearing", M.A.Sc., Carleton University (Canada), 2007, 130 pages; AAT MR33665
36. Samsi Rohan, "A Probabilistic Framework for Fault Detection in Induction Motors", PhD., The Pennsylvania State University, 2006, 156 pages, AAT 3248392
37. Sanger, T., "Optimal Unsupervised Learning in a Single Layer Linear Feedforward neural Network", Neural Networks 12,459-473, 1989
38. Smyth, P. , "Hidden Markov Models and Neural Networks for Fault Detection in Dynamic Systems", Neural Networks for Processing , III,. Proceedings of the 1993 IEEE-SP Workshop, pp. 582-592, 1993
39. Status Technologies, "MotorStatus Information Sheet", 1997
40. Su, Y.T., Lin , S.J., "On Initial Fault Detection of a Tapered Roller Bearing: Frequency Domain Analysis" , Journal of Sound and Vibration, 155(1), pp. 75-84, 1992
41. Subrahmanyam, M., Sujatha, C., "Using Neural Networks for the Diagnosis of Localized Defects in Ball Bearings", Tribology International, Vol 30, No.10, pp.739-752,1997
42. Tiwari, A., Ballal, P., and Lewis, F. L., "Energy-efficient wireless sensor network design and implementation for condition-based maintenance.," ACM Trans. Sens. Netw. 3, 1, Article 1 (March 2007), 23 pages. DOI = 10.1145/1210669.1210670.
43. Vachtsevanos George, Lewis Frank, Roemer Michael, Hess Andrew, Wu Biqing, "Intelligent Fault Diagnosis and Prognosis for Engineering Systems", John Wiley & Sons, Inc, 2006
44. Zaknich, A., 2003, Neural networks for intelligent signal processing: World Scientific, Singapore, 508p.
45. Zhou, Wei, "Incipient bearing fault detection for electric machines using stator current noise cancellation", Ph.D., Georgia Institute of Technology, 2007, 184 pages; AAT 3294583