A dynamical systems approach to damage evolution tracking, part 1: Description and experimental application

Journal of Vibration and Acoustics, Transactions of the ASME

Volumn 124, Issue 2, 2002, Pages 250-257

  Chelidze, David ^a   Cusumano, Joseph P ^b   Chatterjee, Anindya ^c  

^a UNIVERSITY OF RHODE ISLAND   (United States)

^b The Pennsylvania State University   (United States)

^c INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC BATTERIES; ELECTRIC DISCHARGES; MATHEMATICAL MODELS; VECTORS; VIBRATIONS (MECHANICAL);

VIBRATING BEAM SYSTEMS;

ELECTROMECHANICAL DEVICES;

EID: 0036554877     PISSN: 10489002     EISSN: 15288927     Source Type: Journal    
DOI: 10.1115/1.1456908     Document Type: Article

References (35)

1. Qu, L., Xie, A., and Li, X., 1993, “Study and Performance Evaluation of Some Nonlinear Diagnostic Methods for Large Rotating Machinery,” Mech. Mach. Theory, 28(5), pp. 699-713.
2. Ma, J., and Li, C. J., 1995, “On Gear Localized Defect Detection by Demodulation of Vibrations-A Comparison Study,” E. Kannatey-Asibu, J., ed., Proc. of Symp. on Mechatronics for Manufacturing in ASME International Mechanical Engineering Congress and Exposition, MED 2-1, pp. 565-576.
3. McFadden, P. D., and Smith, J. D., 1985, “A Signal Processing Technique for Detecting Local Defects in a Gear From the Signal Averaging of Vibration,” Proc. Inst. Mech. Eng., Part C: Mech. Eng. Sci., 199(c4), ImechE-1985.
4. Robert, T. S., and Lawrence, J. M., 1986, “Detection, Diagnosis and Prognosis of Rotating Machinery,” Proc. of the 41st Meeting of the Mech. Failures Prevention Group, Naval Air Test Center, Patuxent River, Maryland.
5. McFadden, P. D., and Wang, W. J., 1993, “Early Detection of Gear Failure by Vibration Analysis-I. Calculation of the Time-Frequency Distribution,” Mech. Syst. Signal Process., 37(3), pp. 193-201.
6. Liu, B., Ling, S., and Meng, Q., 1997, “Machinery Diagnosis Based on Wavelet Packets,” J. Vib. Control, 3, pp. 5-17.
7. Baydar, N., Ball, A., and Kruger, U., 1999, “Detection of Incipient Tooth Defect in Helical Gears Using Principal Components,” Starr, A. G., Leung, A. Y. T., Wright, J. R., and Sandoz, D. J., eds. Integrating Dynamics, Condition Monitoring and Control for the 21st Century, A. A. Balkema, Rotterdam, Brookfield, pp. 93-100.
8. Staszewski, W. J., Worden, K., and Tomlinson, G. R., 1997, “Time-frequency Analysis in Gearbox Fault Detection Using the Wigner-Ville Distribution and Pattern Recognition,” Mech. Syst. Signal Process., 11(5), pp. 673-692.
9. Staszewski, W. J., and Tomlinson, G. R., 1994, “Application of the Wavelet Transform to Fault Detection in a Spur Gear,” Mech. Syst. Signal Process., 8(3), pp. 289-307.
10. Kim, T, and Li, C. J., 1995, “Feedforward Neural Networks for Fault Diagnosis and Severity Assessment of a Screw Compressor,” Mech. Syst. Signal Process., 9(5), pp. 485-496.
11. Chancellor, R. S., Alexander, R., and Noah, S., 1996, “Detecting Parameter Changes Using Experimental Nonlinear Dynamics and Chaos,” ASME J. Vibr. Acoust., 118, pp. 375-383.
12. Isermann, R., 1984, “Process Fault Detection Based on Modeling and Estimation Methods-A Survey,” Automatica, 20(4), pp. 387-404.
13. Patton, R., Frank, P, and Clark, P, 1989, Fault Diagnosis in Dynamical Systems: Theory and Application, Prinston Hall International (UK) Ltd.
14. Gertler, J., 1988, “Survey of Model-Based Failure Detection and Isolation in Complex Plants,” IEEE Control Syst. Mag., 8(6), pp. 3-11.
15. Nijmeijer, H., and Fossen, T. I., 1999, “New Directions in Nonlinear Observer Design,” Lecture Notes in Control and Information Sciences, Springer-Verlag, London, pp. 351-466.
16. Ashton, S. A., and Shields, D. N., 1999, “Fault Detection Observer for a Class of Nonlinear Systems,” Nijmeijer, H., and Fossen, T. I., eds., New Directions in Nonlinear Observer Design, Lecture Notes in Control and Information Sciences, Springer-Verlag, London, pp. 353-373.
17. Jeon, Y. C., and Li, C. J., 1995, “Non-linear ARX Model Based Kullback Index for Fault Detection of a Screw Compressor,” Mech. Syst. Signal Process., 9(4), pp. 341-358.
18. McGhee, J., Henderson, I. A., and Baird, A., 1997, Neural Networks Applied for the Identification and Fault Diagnosis of Process Valves and Actuators, J. of Int. Measr. Confederation, 20(4), pp. 267-275.
19. Allessandri, A., and Parisini, T., 1997, “Model-Based Fault Diagnosis Using Nonlinear Estimators: A Neural Approach,” Proceedings of the American Control Conference, 2, pp. 903-907.
20. Li, C. J., and Fan, Y., 1997, “Recurrent Neural Networks for Fault Diagnosis and Severity Assessment of a Screw Compressor,” DSC ASME Dynamic Systems & Control Division, 61, pp. 257-264.
21. Cusumano, J. P., and Kimble, B., 1995, “A Stochastic Interrogation Method for Experimental Measurements of Global Dynamics and Basic Evolution: Application to a Two-Well Oscillator,” Nonlinear Dyn., 8, pp. 213-235.
22. Kimble, B. W., and Cusumano, J. P, 1996, “Theoretical and Numerical Validation of the Stochastic Interrogation Experimental Method,” J. Vib. Control, 2, pp. 323-348.
23. Takens, F., 1981, “Detecting Strange Attractor in Turbulence,” Rand, D. A., and Young, L. S., eds. Dynamical Systems and Turbulence, Warwick, Springer Lecture Notes in Mathematics, Springer-Verlag, Berlin, pp. 336-381.
24. Sauer, T., Yorke, J. A., and Casdagli, M., 1991, “Embedology,” J. Stat. Phys., 65(3-4), pp. 579-616.
25. Fraser, A. M., and Swinney, H. L., 1986, “Independent Coordinates for Strange Attractors from Mutual Information,” Phys. Rev. A, 33(2), pp. 1134-1140.
26. Kennel, M. B., Brown, R., and Abarbanel, H. D. I., 1992, “Determining Embedding Dimension for Phase-Space Reconstruction Using a Geometric Construction,” Phys. Rev. A, 45(6), pp. 3403-3411.
27. Ott, E., 1993, Chaos in Dynamical Systems, Cambridge, New York.
28. Moon, F. C., and Holms, P., 1979, A Magnetoelastic Strange Attractors, J. Sound Vib., 65(2), pp. 275-296.
29. Feeny, B. F., Yuan, C. M., and Cusumano, J. P, 2000, “Parametric Identification of an Experimental Magneto-Elastic Oscillator,” J. Sound Vib. (accepted).
30. Friedman, J. H., Bentley, J. L., and Finkel, R., 1977, “An Algorithm for Finding Best Matches in Logarithmic Expected Time,” ACM Trans. Math. Softw., 3(2), pp. 209-226.
31. Golnaraghi, M., Lin, D., and Fromme, P., 1995, “Gear Damage Detection using Chaotic Dynamics Techniques: A Preliminary Study,” Proc. of the 1995 ASME Des. Tech. Conf., Symposium on Time-varying Systems and Structures, Boston, MA, Sep 17-20, pp. 121-127.
32. Craig, C., Neilson, D., and Penman, J., 2000, “The Use of Correlation Dimension in Condition Monitoring of Systems with Clearance,” J. Sound Vib., 231(1), pp. 1-17.
33. Jiang, J. D., Chen, J., and Qu, L. S., 1999, “The Application of Correlation Dimension in Gearbox Condition Monitoring,” J. Sound Vib., 223(4), pp. 529-541.
34. Logan, D. B., and Mathew, J., 2000, “Using the Correlation Dimension for Vibration Fault Diagnosis of Rolling Element Bearing-I. Basic Concepts,” Mech. Syst. Signal Process., 10(3), pp. 241-250.
35. Rosenstein, M. T., Collins, J. J., and DeLuca, C. J., 1993, “A Practical Method for Calculating Largest Lyapunov Exponents From Small Data Sets,” Physica D, 65(1-2), pp. 117-134.