Fault detection in variable speed machinery: Statistical parameterization

Journal of Sound and Vibration

Volumn 327, Issue 3-5, 2009, Pages 623-646

  McBain, Jordan ^a   Timusk, Markus ^a  

^a LAURENTIAN UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED CONDITIONS; CLASSIFICATION APPROACH; CLASSIFICATION ERRORS; FEATURE VECTORS; MASK FAULTS; MEASURABLE VARIABLES; MONITORING SYSTEM; MONITORING TECHNIQUES; STANDARD CONDITIONS; STATISTICAL DECISION; STATISTICAL PARAMETERS; SUPPORT VECTOR; TRAINING DATA; VARIABLE SPEED; VIBRATION RESPONSE;

BINS; CONDITION MONITORING; COVARIANCE MATRIX; DECISION MAKING; FAULT DETECTION; GAME THEORY; MACHINERY;

SPEED;

EID: 69049087555     PISSN: 0022460X     EISSN: 10958568     Source Type: Journal    
DOI: 10.1016/j.jsv.2009.07.025     Document Type: Article

References (36)

1. Sohn H., Worden K., and Farrar C.R. Novelty detection in a changing environment: regression and interpolation approaches. Journal of Sound and Vibration 258 4 (2002) 741-761
2. Stack J.R., Habetler T.G., and Harley R.G. Effects of machine speed on the development and detection of rolling element bearing faults. IEEE Power Electronics Letters 1 (2003) 19-21
3. Randall R.B., Antoni J., and Chobsaard S. The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mechanical Systems and Signal Processing 15 (2001) 945-962
4. Choy F.K., Huang S., Zakrajsek J.J., Handschuh R.F., and Townsend D.P. Vibration signature analysis of a faulted gear transmission system. Journal of Propulsion and Power 12 (1996) 289-295
5. Ivanov Y.Y., and Meltzer G. Fault detection in gear drives with non-stationary rotational speed-part I: the time-frequency approach. Mechanical Systems and Signal Processing 17 (2003) 1033-1047
6. Ivanov Y.Y., and Meltzer G. Fault detection in gear drives with non-stationary rotational speed-part II: the time-frequency approach. Mechanical Systems and Signal Processing 17 (2003) 273-283
7. Kar C., and Mohanty A.R. Vibration and current transient monitoring for gearbox fault detection using multiresolution Fourier transform. Journal of Sound and Vibration 311 (2008) 109-132
8. Yang W., and Tavner P.J. Empirical mode decomposition an adaptive approach for interpreting shaft vibratory signals of large rotating machinery. Journal of Sound and Vibration 321 (2009) 1144-1170
9. Saavedra P., and Vicuna C.J. Online monitoring system for mining shovels focused on vibration analysis. Canadian Institute of Mining 2 (2007) 101
10. Charles P., Sinha J.K., Gu F., Lidstone L., and Ball A.D. Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis. Journal of Sound and Vibration 321 (2009) 1171-1185
11. Baydar N., and Ball A. Detection of gear deterioration under varying load conditions by using the instantaneous power spectrum. Mechanical Systems and Signal Processing 14 (2000) 907-921
12. Mechefske C.K., and Liu L. Fault detection and diagnosis in variable speed machines. International Journal of Condition Monitoring and Diagnostic Engineering Management 5 (2002) 29-40
13. Roy N., and Ganguli R. Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring. Applied Soft Computing Journal 6 (2006) 154-169
14. Roy N., and Ganguli R. Helicopter rotor blade frequency evolution with damage growth and signal processing. Journal of Sound and Vibration 283 (2005) 821-851
15. K. Worden, C. Surace, Some aspects of novelty detection methods, Proceedings of the Third International Conference on Modern Practice in Stress and Vibration, Dublin (1997) 89.
16. M.A. Timusk, M.G. Lipsett, C.K. Mechefske, Automated duty cycle classification for online monitoring systems, 2007 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC2007, v 1 PART A, 2008, pp. 563-573.
17. Timusk M., Lipsett M., and Mechefske C.K. Fault detection using transient machine signals. Mechanical Systems and Signal Processing 22 (2008) 1724
18. M.A. Timusk, A Unified Method for Anomaly Detection in Unsteady Systems, Ph.D. Thesis, University of Queens, 2006.
19. Timusk M.A., Mechefske C.K., and Lipsett M. A pragmatic framework for on-line neural network based detection of machinery. International Journal of Condition Monitoring and Diagnostic Engineering Management 8 (2005) 35-41
20. Ni Y.Q., Ko J.M., Hua X.G., and Zimroz R. Variability of measured modal frequencies of a cable-stayed bridge under different wind conditions. Smart Structures and Systems 3 (2007) 341
21. Sohn H., Dzwonczyk M., Straser E.G., Kiremidjian A.S., Law K.H., and Meng T. An experimental study of temperature effect on modal parameters of the Alamosa Canyon Bridge. Earthquake Engineering and Structural Dynamics 28 (1999) 879-897
22. Hua X.G., Ni Y.Q., Ko J.M., and Wong K.Y. Modeling of temperature-frequency correlation using combined principal component analysis and support vector regression technique. Journal of Computing and Civil Engineering 21 (2007) 122-135
23. Stander C.J., Heyns P.S., and Schoombie W. Using vibration monitoring for local fault detection on gears operating under fluctuating load conditions. Mechanical Systems and Signal Processing 16 (2002) 1005-1024
24. Worden K., Sohn H., and Farrar C.R. Novelty detection in a changing environment: regression and interpolation approaches. Journal of Sound and Vibration 258 (2002) 741-761
25. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification. second ed. (2001), Wiley, New York, NY
26. Markou M., and Singh S. Novelty detection: a review-part 1: statistical approaches. Signal Processing 83 (2003) 2481-2497
27. Ni Y.Q., Hua X.G., Fan K.Q., and Ko J.M. Correlating modal properties with temperature using long-term monitoring data and support vector machine technique. Engineering Structures 27 (2005) 1762-1773
28. R.B. Randall, New method of modeling gear faults, American Society of Mechanical Engineering Papers (1981) 9.
29. Randall R.B. State of the art in monitoring rotating machinery-part 1. Journal of Sound and Vibration 38 (2004) 14-21
30. Peebles P.Z. Probability Random Variables and Random Signal Principles. fourth ed. (2001), McGraw-Hill Higher Education, New York, NY
31. K. Worden, G. Manson, N.R.J. Fieller, S. Curtis, Damage detection using multivariate statistics. Part I: outlier analysis, Proceedings of the 23rd International Conference on Noise and Vibration Engineering, ISMA (1998) 189.
32. Filzmoser P., Garrett R.G., and Reimann C. Multivariate outlier detection in exploration geochemistry. Computers and Geosciences 31 (2005) 579
33. Tax D.M.J., and Duin R.P.W. Support vector domain description. Pattern Recognition Letters 20 (1999) 1191-1199
34. Eisenmann R.C. Machinery Malfunction Diagnosis and Correction: Vibration Analysis and Troubleshooting for the Process Industries (1998), Prentice Hall PTR, Upper Saddle River, New Jersey
35. Principe J.C., Euliano N.R., and Lefebvre W.C. Neural and Adaptive Systems (2000), Wiley, Toronto, ON
36. D.M.J. Tax, One-class Classification: Concept Learning in the Absence of Counter-examples, Ph.D. Thesis, University of Delft of Technology, 2001.