Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches

Mechanical Systems and Signal Processing

Volumn 24, Issue 8, 2010, Pages 2972-2984

  Ge, Zhiqiang ^a   Kruger, Uwe ^b   Lamont, Lisa ^b   Xie, Lei ^a   Song, Zhihuan ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

^b KHALIFA UNIVERSITY   (United Arab Emirates)

Author keywords

Dynamic statistical process monitoring; Fault detection; Gearbox systems; Non Gaussian vibration signals

Indexed keywords

CROSS CORRELATIONS; DATA MODELS; DYNAMIC DATA; DYNAMIC DATA STRUCTURE; EXPERIMENTAL DATA; GEARBOX SYSTEMS; INCIPIENT FAULTS; LOCAL APPROACHES; MECHANICAL SYSTEMS; MULTIVARIATE STATISTICAL METHOD; NON-GAUSSIAN; NON-GAUSSIAN VIBRATIONS; RECORDED SIGNALS; SERIAL CORRELATION; STATISTICAL PROCESS MONITORING; SUPPORT VECTOR DATA DESCRIPTION; VIBRATION SIGNAL;

DATA HANDLING; DATA STRUCTURES; FAULT DETECTION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); IMAGE SEGMENTATION; INDEPENDENT COMPONENT ANALYSIS; PROCESS MONITORING; SIGNAL DETECTION; STATISTICAL PROCESS CONTROL; STATISTICS; VIBRATIONS (MECHANICAL);

MULTIVARIANT ANALYSIS;

EID: 78049444138     PISSN: 08883270     EISSN: 10961216     Source Type: Journal    
DOI: 10.1016/j.ymssp.2010.03.015     Document Type: Article

References (37)

1. W.J. Staszewski, and G.R. Tomlinson Timefrequency analysis in gearbox fault detection using the WignerVille distribution and pattern recognition Mechanical Systems and Signal Processing 11 5 1997 673 692
2. N. Baydar, and A.D. Ball A comparative study of acoustics and vibration signals in detection of gear failures using WignerVille distribution Mechanical Systems and Signal Processing 15 6 2001 1091 1107
3. C.J. Stander, P.S. Heyns, and W. Schoombie Using vibration monitoring for local fault detection on gears operating under fluctuating load conditions Mechanical Systems and Signal Processing 16 6 2002 1005 1024
4. C.K. Tan, and D. Mba Limitation of acoustic emission for identifying seeded defects in gearboxes Nondestructive Evaluation 24 1 2005 11 28
5. Q. Hu, Z. He, Z. Zhang, and Y. Zi Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble Mechanical Systems and Signal Processing 21 2 2007 688 705
6. J.D. Wu, C.C. Hsu, and G.Z. Wu Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference Expert Systems with Applications 36 3 2008 6244 6255
7. W. Wang Autoregressive model-based diagnostics for gears and bearings Insight-Non-Destructive Testing and Condition Monitoring 50 8 2008 414 418
8. B. Eftekharnejad, and D. Mba Acoustic emission signals associated with damaged helical gears Insight-Non-Destructive Testing and Condition Monitoring 50 8 2008 450 453
9. N. Vincenzo, Q. Guiseppe, and F. Aniello The detection of gear noise computed by integrating the Fourier and wavelet methods WSEAS Transactions on Signal Processing 3 4 2008 60 67
10. W. Bartelmus, and R. Zimroz Vibration condition monitoring of planetary gearbox under varying external load Mechanical Systems and Signal Processing 23 1 2009 246 257
11. M.L. Fugate, H. Sohn, and C.R. Farrar Vibration-based damage detection using statistical process control Mechanical System and Signal Processing 15 4 2001 707 721
12. N. Baydar, A.D. Ball, U. Kruger, Detection of incipient tooth defect in helical gears using principal components, in: Proceedings of the First International Conference on the Integrating of Dynamics, Monitoring and Control, Manchester, U.K., 1999, pp. 93100.
13. N. Baydar, Q. Chen, A.D. Ball, and U. Kruger Detection of Incipient Tooth Defect in Helical Gears Using Multivariate Statistics Mechanical Systems and Signal Processing 15 2 2001 303 321
14. A. Malhi, and R. Gao PCA-based feature selection scheme for machine defect classification IEEE Transaction on Instrumentation and Measurement 53 2004 1517 1525
15. Q.B. He, F.R. Kong, and R.Q. Yan Subspace-based gearbox condition monitoring by kernel principal component analysis Mechanical Systems and Signal Processing 21 4 2007 1755 1772
16. Q.B. He, R.Q. Yan, F.R. Kong, and R.X. Du Machine condition monitoring using principal component representations Mechanical Systems and Signal Processing 23 4 2009 446 466
17. W. Ku, R.H. Storer, and C. Georgakis Disturbance rejection and isolation by dynamic principal component analysis Chemometrics & Intelligent Laboratory Systems 30 1995 179 196
18. L. Xie, U. Kruger, D. Lieftucht, T. Littler, Q. Chen, and S.Q. Wang Statistical monitoring of dynamic multivariate processes part1 modeling autocorrelation and cross-correlation Industrial and Engineering Chemistry Research 45 2006 1659 1676
19. Q.B. He, Z.H. Feng, and F.R. Kong Detection of signal transients using independent component analysis and its application in gearbox condition monitoring Mechanical Systems and Signal Processing 21 2 2007 2056 2071
20. A. Widodo, B.S. Yang, and T. Han Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors Expert systems with applications 32 2 2007 299 312
21. A. Widodo, and B.S. Yang Support vector machine in machine condition monitoring and fault diagnosis Mechanical Systems and Signal Processing 21 2 2007 2560 2574
22. J. Chen, and K. Liu On-line batch process monitoring using dynamic PCA and dynamic PLS models Chemical Engineering Science 14 2002 63 75
23. U. Kruger, Y.Q. Zhou, and G.W. Irwin Improved principal component monitoring of large-scale processes Journal of process control 14 2004 879 888
24. P.V. Overschee, and D.M. Moor Subspace identification for linear systems 1996 Kluwer Academic Publishers
25. W. Li, and S.J. Qin Consistent dynamic PCA based on error-in-variable subspace identification Journal of Process Control 11 2001 661 676
26. X. Liu, L. Xie, U. Kruger, T. Littler, and S.Q. Wang Statistical-based monitoring of multivariate non-Gaussian systems AIChE Journal 54 9 2008 2379 2391
27. J.M. Lee, C.K. Yoo, and I.B. Lee Statistical monitoring of dynamic processes based on dynamic independent component analysis Chemical Engineering Science 59 14 2004 2995 3006
28. M. Basseville, and I. Nikiforov Detection of abrupt changestheory and application 1993 Prentice Hall N.J.
29. M. Basseville On-board component fault detection and isolation using the statistical local approach Automatica 34 1998 1391 1415 (Pubitemid 128400138)
30. U. Kruger, and G. Dimitriadis Diagnosis of process faults in chemical systems using a local partial least squares approach AIChE Journal 54 2008 2581 2596
31. Q.H. Zhang, M. Basseville, and A. Benveniste Early warning of slight changes in systems and plants with application to condition based maintenance Automatica 30 1994 95 114
32. M. Basseville, M. Abdelghani, and A. Benveniste Subspace-based fault detection algorithms for vibration monitoring Automatica 36 2000 101 109
33. U. Kruger, S. Kumar, and T. Littler Improved principal component monitoring using the local approach Automatica 43 2007 1532 1542
34. C.M. Jarque, and A.K. Bera A test for normality of observations and regression residuals International Statistical Review 55 2 1987 267 274
35. A. Hyvarinen, and E. Oja Independent component analysis: algorithms and applications Neural Network 13 2000 411 430 (Pubitemid 30447427)
36. D.M.J. Tax, and R.P.W. Duin Support vector domain description Pattern Recognition Letters 20 1113 1999 1191 1199
37. D. Bauer, M. Deistler, and W. Scherrer Consistency and asymptotic normality of some subspace algorithms for systems without observed inputs Automatica 35 7 1999 1243 1254