A wavelet packet and residual analysis based method for hydraulic pump health diagnosis

Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering

Volumn 220, Issue 6, 2006, Pages 735-745

  Gao, Yingjie ^a   Zhang, Qin ^b  

^a YANSHAN UNIVERSITY   (China)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Fault diagnosis; Health monitoring; Hydraulic pump; Residual analysis; Wavelet transform

Indexed keywords

CONDITION MONITORING; FAILURE ANALYSIS; WAVELET DECOMPOSITION;

HEALTH MONITORING; PUMP DISCHARGE PRESSURE; RESIDUAL ANALYSIS;

PUMPS;

EID: 33947572505     PISSN: 09544070     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544070JAUTO253     Document Type: Article

References (17)

1. Jardine, A. K. S., Banjevic, D., Wiseman, M., Buck, S., and Joseph, T. Optimizing a mine haul truck wheel motors' condition monitoring program: use of proportional hazards modeling. J. Qual. Maintenance Engng, 2001, 7(4), 286-301.
2. Prabhakaran, A. and Jagga, C. R. Condition monitoring of steam turbine-generator through contamination analysis of used lubricating oil. Tribology Int., 1999, 32(3), 145-152.
3. Willsky, A. S. A survey of design methods for failure detection in dynamic systems. Automatica, 1976, 12(6), 601-611.
4. Thomson, W. T., Leonard, R. A., Milne, A. J., and Penman, J. Failure identification of offshore induction motor systems using on-condition monitoring. Reliability Engng, 1984, 9(1), 49-64.
5. Frank, P. M. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: a survey and some new results. Automatica, 1990, 26(3), 459-474.
6. Wang, H. and Noriega, J. R. Fault diagnosis for unknown non-linear systems via neural networks and its comparisons and combinations with recursive least-squares based techniques. Proc. IMechE, Part I: J. Syst. and Control Engng, 2001, 215(3), 261-278.
7. Yoshida, H. and Kumar, S. ARX and AFMM model-based on-line real-time data base diagnosis of sudden fault in AHU of VAV system. Energy Conversion and Managmt, 1999, 40(11), 1191-1206.
8. Liu, Z., Yin, X., Zhang, Z., Chen, D., and Chen, W. Online rotor mixed fault diagnosis way based on spectrum analysis of instantaneous power in squirrel cage induction motors. IEEE Trans. Energy Conversion, 2004, 19(3), 485-490.
9. Martin, K. F. A review by discussion of condition monitoring and fault diagnosis in machine tools. Int. J. Mach. Tools Mfg, 1994, 34(4), 527-551.
10. Manring, N. and Johnson, R. Modeling and designing a variable-displacement open-loop pump. Trans. ASME, J. Dynamic Syst. Measmt and Control, 1996, 118(2), 267-271.
11. Javadpour, R. and Knapp, G. M. A fuzzy neural network approach to machine condition monitoring. Computers and Ind. Engng, 2003, 45(2), 323-330.
12. Gao, Y., Zhang, Q., and Kong, X. Wavelet-based pressure analysis for hydraulic pump diagnosis. Trans. ASAE, 2003, 46(4), 969-976.
13. Hancock, K. M. and Zhang, Q. A hybrid approach to hydraulic vane pump condition monitoring and fault detection. Trans. ASAE, 2006 (in press).
14. Lou, X., Kenneth, K. A., and Loparo, A. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech. Syst. and Signal Processing, 2004, 18(5), 1077-1095.
15. Misti, M., Misti, Y., Oppenheim, G., and Poggi, J. Wavelet Toolbox User's Guide (Version 3), 1997 (Mathworks, Inc., Natick, Massachusetts).
16. Gubner, J. A. and Chang, W.-B. Wavelet transforms for discrete-time periodic signals. Signal Processing, 1995, 42(2), 167-180.
17. Taswell, C. and McGill, K. C. Wavelet transform algorithms for finite-duration discrete-time signals. ACM Trans. Math. Software, 1994, 20(3), 398-412.