Application of ANN, Fuzzy Logic and Wavelet Transform in machine fault diagnosis using vibration signal analysis

Journal of Quality in Maintenance Engineering

Volumn 16, Issue 2, 2010, Pages 190-213

  Jayaswal, Pratesh ^a   Verma, S N ^b   Wadhwani, A K ^a  

^a MADHAV INSTITUTE OF TECHNOLOGY AND SCIENCE   (India)

^b UNIVERSITY INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Condition monitoring; Fuzzy logic; Laboratory testing

Indexed keywords

BEARING CONDITIONS; COUPLING FAULTS; EXPERIMENTAL SETUP; FAULT DIAGNOSIS; FAULT DIAGNOSTICS; FAULT SIGNATURE; FUZZY MEMBERSHIP FUNCTION; GEAR FAULTS; HYBRID EXPERT SYSTEM; KNOWLEDGE BASE; LABORATORY INVESTIGATIONS; LABORATORY TESTING; LR-TYPE FUZZY NUMBERS; MACHINE FAULT DIAGNOSIS; MAINTENANCE ENGINEERS; MECHANICAL FAULTS; MONITORING SYSTEM; ON-LINE FAULT DIAGNOSIS; ROLLING ELEMENT BEARING; TRIANGULAR MEMBERSHIP FUNCTIONS; VIBRATION SIGNAL ANALYSIS; WAVELETS DECOMPOSITION;

BEARINGS (MACHINE PARTS); BEARINGS (STRUCTURAL); CONDITION MONITORING; COUPLINGS; ENGINEERS; EXPERT SYSTEMS; FAILURE ANALYSIS; FUZZY RULES; FUZZY SETS; FUZZY SYSTEMS; HYBRID SYSTEMS; MAINTENANCE; MEMBERSHIP FUNCTIONS; QUALITY ASSURANCE; SIGNAL ANALYSIS; VIBRATION ANALYSIS; WAVELET DECOMPOSITION;

FUZZY LOGIC;

EID: 77953261575     PISSN: 13552511     EISSN: None     Source Type: Journal    
DOI: 10.1108/13552511011048922     Document Type: Article

References (57)

1. Abu-Zahra, N.H. and Yu, G. (2003), "Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves", International Journal of Machine Tool and Manufacturer, Vol.43, pp. 337-343
2. Alguindigue, I.E., Wicz-Buczak, A.E. and Uhrig, R.E. (1993), "Monitoring and diagnosis of rolling element bearings using artificial neural networks", IEEE Transactions on Industrial Electronics, Vol.40 No.2, pp. 209-217
3. Badyopadhya, A., Mandal, S.D. and Pal, B. (n.d.), Real Time Condition Monitoring System Using Vibration Analysis for Turbine Bearing, Speech and Signal Processing Group, Calcutta.
4. Becerikli, Y. (2004), "On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory", Neural Comput. and Applic., Vol.13, pp. 339-351
5. Biqing, W., Saxena, A., Romano, P. and George, V. (2004), Vibration Monitoring and Fault Diagnosis of Helicopter Planetary Gears, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA.
6. Bloch, H.P. and Geitner, F.K. (1983), Machinery Failure Analysis and Troubleshooting, chapter 5, Gulf Publishing Company, Houston, TX.
7. Brian, T.H., Gao, R.X. and Zhang, L. (2004), "Customized wavelet for bearing defect detection", Journal of Dynamic Systems, Measurement, and Control, Transaction of ASME, Vol.126, pp. 740-745
8. Cheng, L., Zhihuman, S. and Ping, L. (2004), "Bearing fault identification via wavelet packet transform and rough set theory", Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, June 15-19, pp. 1663-1666
9. Dempsey, P.J. and Afjeh, A. (2002), "Integrating oil debris and vibration gear damage detection technologies using fuzzy logic", paper presented at the 58th International Annual Forum and Technology Display sponsored by the American Helicopter Society, Montreal, June 11-13.
10. Dyke, J.V.P.E. (1998), "Using an expert system for precision machine tool diagnostics: a case study", DLI Engineering.
11. Fan, X. and Zuo, M.J. (2006), "Gearbox fault detection using Hilbert and wavelet packet transform", Mechanical Systems and Signal Processing, Vol.20 No.4, pp. 966-982
12. Fillippetti, F., Franceschini, G. and Tassoni, C. (1995), "Neural network aided online diagnostic of induction motor rotor faults", IEEE Transactions on Industrial Electronics, Vol.31 No.4, July/August, pp. 892-899
13. Fillippetti, F., Franceschini, G. and Tassoni, C. (2000), "Recent developments of induction motor drives fault diagnostics using AI techniques", IEEE Transactions on Industrial Electronics, Vol.47 No.5, October, pp. 994-1003.
14. Goddu, G.G., Li, G., Chow, M.Y. and Hung, J.C. (1998), "Motor bearing fault diagnosis by a fundamental frequency amplitude-based fuzzy decision system", Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (IECON '98), Aachen, August-September, pp. 1961-1965
15. Jayaswal, P., Wadhwani, A.K. and Mulchandani, K.B. (2008a), "Vibration-based monitoring and analysis", Asset Management and Maintenance Journal, Vol.21 No.1, January, pp. 52-57
16. Jayaswal, P., Wadhwani, A.K. and Mulchandani, K.B. (2008b), "Machine fault signature analysis", International Journal of Rotating Machines.
17. Jun, P. and Lias, M.F. (2007), "A wavelet-based method for extracting rolling element bearing vibration signal envelope", Proceedings of the WSEAS International Conference on Computer Engineering and Applications, Gold Coast, January, pp. 181-186
18. Kowalski, C.T. and Kowalska, T.O. (2003), "Neural networks application for induction motor faults diagnosis", Mathematics and Computers in Simulation, Vol.63.
19. Lebold, N., McClintic, K., Campbell, R., Byington, C. and Maynard, K. (2000), "Review of vibration analysis method for gearbox diagnostic and prognostics", Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, VA, May, pp. 623-634
20. Lei, Y., Kire, M. and Kenny, T.W. (2003), "Statistical damage detection using time series analysis on a structural health monitoring benchmark problem", Proceedings of the 9th International Conference on Applications of Statistics and Probability in Civil Engineering, San Francisco, CA, July.
21. Li, B., Chow, M.Y. and Tipsuwan, Y. (2000), "Neural-network-based motor rolling bearing fault diagnosis", IEEE Transactions on Industrial Electronics, Vol.47 No.5, October, pp. 1060-1069
22. Li, B., Goddu, G. and Chow, M.Y. (1998), "Detection of common motor bearing faults using frequency-domain vibration signals and a neural network-based approach", Proceedings of the American Control Conference, Philadelphia, PA, June, Vol.4, pp. 2032-2036
23. Li, S. and Elbestawl, M.A. (1996), "Fuzzy leistering for automated tool condition monitoring in machining", System and Signal Processing, pp. 533-550 (Pubitemid 126360950)
24. Lin, J., Zuo, M.J. and Fyfe, K.R. (2004), "Mechanical fault detection based on the wavelet de-noising technique", Journal of Vibration and Acoustics, Vol.126 No.1, pp. 9-16.
25. Lindh, T., Ahola, J., Spatenka, P. and Rautiainen, A.-L. (n.d.), Automatic Bearing Fault Classification Combining Statistical Classification and Fuzzy Logic, Lappeenranta University of Technology, Lappeenranta.
26. Mann, L., Saxena, A. and Knapp, G.M. (1995), "Statistical-based or condition-based preventive maintenance", Journal of Quality in Maintenance Engineering, Vol.1 No.1, pp. 46-59.
27. Mohanta, H.K. and Gupta, R.K. (2005), "Application of wavelet transform in control: a review", The ICFAI Journal of Science and Technology, Vol.1, March, pp. 7-26.
28. Nadgir, A. and Ozel, T. (2000), "Neural network modeling of flank wear for tool conditions monitoring in orthogonal cutting of hardened steels", paper presented at 4th International Conference on Engineering and Automation, Orlando, FL.
29. Norton, M.P. and Karczub, D.G. (2003), Fundamentals of Noise and Vibration Analysis for Engineers, 2nd ed., chapter 8, University Press, Cambridge.
30. Ocak, H. and Loparo, K.A. (2004), "Estimating the running speed and the bearing key frequencies of an instruction motor from vibration data", Mechanical Systems and Signal Processing, Vol.18 No.3, pp. 515-533
31. Pemean, J. and Yin, C.V. (1994), "Feasibility of using unsupervised learning artificial neural networks, for the condition monitoring of electrical machines", IEE Proceedings of Electronic Power Application.
32. Peng, Z.K. and Chu, F.L. (2004), "Application of the wavelet transform in machine condition monitoring and fault diagnosis: a review with bibliography", Mechanical System and Signal Processing, Vol.18, pp. 199-221.
33. Plenge, M., Lammering, R., Walz, T. and Ettemeyer, A. (2004), "Optical inspection techniques for vibration analysis and defect indication in railways", Proceedings of the 16th WCNDT World Conference on NDT, Montreal, August-September.
34. Poyhonen, S., Negrea, M., Arkkio, A., Hyotyniemi, H. and Koivo, H. (2002), "Support vector classification for fault diagnosis of an electrical machine", Proceedings of the 6th International Conference on Signal Processing (ICSP '02), Vol.2, Helsinki University of Technology, Helsinki, August, pp. 1719-1722
35. Purushotham, V., Narayanan, S. and Prasad, A.N. (2005), "Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model-based fault recognition", NDT and E International, Vol.38 No.8, December, pp. 654-664
36. Rajasekaran, S. and Vijaylakshmi, P.G.A. (2004), Neural Networks Fuzzy Logic, and Genetic Algorithms, Prentice-Hall of India Private Limited, New Delhi, chapter 1, p. 3.
37. Renwick, J.T. and Babson, P.E. (1985), "Vibration analysis - a proven technique as a production maintenance tool", IEEE Transactions on Industry Applications, Vol.21 No.2, pp. 324-332
38. Sahinkaya, M.N., Cole, M.O.T. and Burrows, C.R. (2001), "Fault detection and tolerance in synchronous vibration control of rotor-magnetic bearing system", Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol.215 No.12, pp. 1401-1416
39. Samanta, S., Al-Balushi, K.R. and Al-Araimi, S.A. (2004), "Bearing fault detection using artificial neural networks and genetic algorithm", EURASIP Journal ofApplied Signal Processing, pp. 366-377
40. Shao, Y. and Nezu, K. (2000), "Prognosis of remaining life using neural networks", Proceedings of Institution of Mechanical Engineers, Vol.214, part I, pp. 217-230
41. Shi, D.F., Wang, W.J. and Qu, L.S. (2004), "Defect detection for bearings using envelope spectra of wavelet transform", Journal of Vibration and Acoustics, Vol.126 No.4, pp. 567-573
42. Shikari, B. and Sadiwala, C.M. (2004), Automation in Condition-Based Maintenance Using Vibration Analysis, MANIT, Bhopal.
43. Sima, J. (1995), "Neural expert systems", Neural Networks, Vol.8 No.2, pp. 261-271
44. Smith, A.E. and Coit, D.W. (n.d.), A Neural Network Approach to Condition-Based Maintenance: Case Study of Airport Ground Transportation Vehicles, Department of Industrial and Systems Engineering, Auburn University, Auburn, AL.
45. Sohn, H. and Farrar, C.R. (2001), "Damage diagnosis using time series analysis of vibration signals", Journal of Smart Material and Structures Institute of Physics, Vol.10, pp. 446-451.
46. Stack, J.R., Habetler, T.G. and Harley, R.G. (2003), "Fault classification and fault signature production for rolling element bearings", Proceedings of the 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED '03), Atlanta, GA, August, pp. 172-176.
47. Sun, Q., Chen, P., Zhang, D. and Xi, F. (2004), "Pattern recognition for automatic machinery fault diagnosis", Journal of Vibration and Acoustics, Vol.126 No.2, pp. 307-316
48. Tansel, I.N., Mekdeci, C. and McLaughlin, C. (1995), "Detection of tool failure in end milling with wavelet transformations and neural networks", International Journal of Tools Manufacturing, Vol.35, pp. 1137-1147
49. Tomsovic, K. and Barr, B. (n.d.), Fuzzy Information Approaches to Equipment Condition Motoring and Diagnosis, School of Electrical Eng. and Comp. Science, Washington State University, Washington, DC.
50. Torbatian, M., Kahaei, M.H. and Poshta, J. (n.d.), BearingFault Detection usingLevel-Dependent Noise Reduction Technique, Department of Electrical Engineering, Iran University of Science and Technology, Tehran.
51. Verma, S.P. and Balan, A. (1998), "Experimental investigations on the stations of electrical machines in relation to vibration and noise problems", IEE Proceedings Electric Power Applications, Vol.145 No.5, pp. 455-461
52. Weqerich, S.W., Wilks, A.D. and Pipke, R.M. (2003), "Nonparametric modeling of vibration signal features for equipment health monitoring", Proceedings ofthe IEEEAerospace Conference, Big Sky, MT, March, Vol.7, pp. 3113-3121
53. Ya, W., Escande, P. and Du, R. (2001), "A new method for real time tool condition monitoring in transfer machining stations", Journal ofManufacturing Science and Engineering, ASME, pp. 339-347
54. Yang, D.M., Stronach, A.F. and MacConnell, P. (2003), "The application of advanced signal-processing techniques to induction motor bearing condition monitoring", Meccanica, Vol.38 No.2, pp. 297-308.
55. Zhang, G.P. (2000), "Neural networks for classification: a survey", IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol.30 No.4, November.
56. Zhang, G., Lee, S., Propes, N., Zhao, Y., Vachtsevanos, G., Thakker, A. and Galie, T. (2002), A Novel Architecture for an Integrated Fault Diagnostic/Prognostic System, Georgia Institute of Technology, Atlanta, GA.
57. Zhenya, H., Meng, W. and Bi, G. (1992), "Neural network and its application on machinery fault diagnosis", Proceedings of the IEEE International Conference on Systems Engineering, Kobe, September, pp. 576-579