Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks

International Journal of Advanced Manufacturing Technology

Volumn 68, Issue 1-4, 2013, Pages 763-773

  Zhang, Zhenyou ^a   Wang, Yi ^b   Wang, Kesheng ^a  

^a NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^b UNIVERSITY OF SUNDERLAND   (United Kingdom)

Author keywords

Artificial neural network; Fault diagnosis and prognosis; Principal component analysis; Wavelet packet coefficient

Indexed keywords

FAILURE ANALYSIS; FAULT DETECTION; PRINCIPAL COMPONENT ANALYSIS; ROTATING MACHINERY; WAVELET TRANSFORMS;

DIAGNOSIS AND PROGNOSIS; HIGHER EFFICIENCY; INTELLIGENT FAULT DIAGNOSIS; MANUFACTURING EQUIPMENT; REAL TIME SIGNAL; STANDARD DEVIATION; WAVELET COEFFICIENTS; WAVELET PACKET COEFFICIENT;

NEURAL NETWORKS;

EID: 84888378568     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-013-4797-0     Document Type: Article

References (37)

1. Hu W, Starr AG, Zhou Z, Leung AYT (2000) A systematic approach to integrated fault diagnosis of flexible manufacturing systems. Int J Mach Tool Manuf 40:1587-1560
2. Kegg RL (1984) On-line machine and process diagnostics. Annals of the CIRP 32(2):469-573
3. Zhou ZD, Chen YP, Fuh JH, Nee AYC (2000) Integrated condition monitoring and fault diagnosis for modern manufacturing system. Annals of CIRP 49(1):387-390
4. Lee J, Ni J, Dragan D, Qiu H, Liao H (2006) Intelligent prognostics tools and e-maintenance. Comput Ind 57:476-489
5. Ripka P, Tipek A (2007) Modern sensors handbook. ISTE, London
6. Samanta B, Al-Balushi KR, Araimi SA (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng Appl Artif Intell 16:657-665
7. Ge M, Du R, Zhang G, Xu Y (2004) Fault diagnosis using support vector machine with an application in sheet metal stamping operations. Mech Syst Signal Process 18:143-159
8. Zhang L, Nandi AK (2007) Fault classification using genetic programming. Mech Syst Signal Process 21:1273-1284
9. Lei Y, He Z, Zi Y, Hu Q (2007) Fault diagnosis of rotating machinery based on multiple ANFIS combination with gas. Mech Syst Signal Process 21:2280-2294
10. Goebel M, Gruenwald L (1999) A survey of data mining and knowledge discovery software tools. ACM SIGKDD 1:20-33
11. Kasabov N (2001) Evolving fuzzy neural networks for supervised/ unsupervised online knowledge-based learning. IEEE Trans Syst Man Cybern B Cybern 31:902-918
12. Marzi H (2004) Real-time fault detection and isolation in industrial machines using learning vector quantization. Proc Inst Mech Eng, Part B: J Eng Manuf 218:949-959
13. Markou M, Singh S (2003) Novelty detection: a review-part 2: neural network based approaches. Signal Process 83:2499-2521
14. Wang K (2002) Intelligent condition monitoring and diagnosis systems. IOS, Amsterdam
15. Sreejith, B., Verma A.K. and Srividya, A. (2008). Fault diagnosis of rolling element bearing using time-domain features and neural networks. The Third International Conference on Industrial and Information Systems, pp. 1-6
16. Iyananda H, Towill DR (1973) Fault diagnosis using time domain measurements. Radio and Electron Engineer 43(9):523-533
17. Chang J, Li T, Li P (2010) The selection of time domain characteristic parameters of rotating machinery fault diagnosis. 2010 Int Conf Logist Syst Intell Manag 1:619-623
18. Brygilewicz V, Wojciechowski J (1998) Time domain fault diagnosis of analogue circuits in the presence of noise. IEE Proc Circ, Devices and Syst 145(2):125-131
19. Chang H (2002) Frequency-domain grouping robust fault diagnosis for analog circuits with uncertainties. Int J Circ Theory and Appl 31(1):65-86
20. Tian X, Lin J, Ken RF, Zuo MJ (2003) Gearbox fault diagnosis using independent component analysis in the frequency domain and wavelet filtering. 2003 IEEE Inter Conf Acoust, Signal Proc 2:245-248
21. Purkait P, Chakravorti S (2002) Time and frequency domain analyses based expert system for impulse fault diagnosis in transformers. IEEE Trans Dielectr Electr Insul 9(3):433-445
22. Wu J, Liu C (2009) An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network. Expert Syst Appl 36:4278-4286
23. Kamal H, Meisam P, Hosein M (2011) Fault diagnosis and classification based on wavelet transform and neural network. Progress in Nucl Energy 53:41-47
24. Saravanan N, Ramachandran KI (2010) Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Syst Appl 37:4168-4181
25. Qiu, C., Zuo, X., Wang C., Wu J., and Zhang, T. (2010). An urban traffic speed fusion method based on principal component analysis and neural network. The 2010 International Joint Conference on Neural Networks, pp. 1-7.
26. Zhu, Z., Ma, Z., Wang, Z. and Jiang, J., (2009). Model study of transformer fault diagnosis based on principal component analysis and neural network. Proceeding of 2009 IEEE International Conference on Networking, Sensing and Control, pp. 936-940
27. Wang K (2005) Applied computational intelligence in intelligent manufacturing systems. Advanced Knowledge International Pty Ltd, Australia
28. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115-133. doi: 10.1007/BF02478259
29. Soman KP, Ramachandran KI (2004) Insight into wavelets from theory to practice, 2nd edn. PHI Learning Private Ltd, Delhi
30. Goumas S, Zervakis M, Pouliezos A, Stavrakakis GS (2001) Intelligent on-line quality control of washing machines using discrete wavelet analysis features and likelihood classification. Eng Appl Artif Intell 14(5):655-666
31. Li X, Qu L, Wen G, Li C (2003) Application of wavelet packet analysis for detection in electro-mechanical systems based on torsional vibration measurement. Mech Syst Signal Proc 17(6):1219-1235. doi: 10.1006/mssp.2002.1517
32. Staszewski WJ, Giacomin J (1997) Application of the wavelet based FRFs to the analysis of non-stationary vehicle data. Proc Int Modal Anal Conf 1:425-431
33. Vafaei S, Rahnejat H (2003) Indicated repeatable runout with wavelet decomposition (IRR-WD) for effective determination of bearing-induced vibration. J Sound and Vib 260(1):67-82. doi: 10.1016/S0022-460X(02)00900-8
34. Pearson K (1901) On lines and planes of closest fit to systems of points in space. Philos Mag 2(6):559-572
35. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumenhart DE, McCelland JL (eds) Parallel distributed processing: explorations in the microstructure of cognition. MIT, Cambridge, pp 318-362
36. May, R., Dandy, G., and Maier, H. (2011). Review of input variable selection methods for artificial neural networks. In: K. Suzuki (ed.) Artificial neural networks-methodological advances and biomedical applications. InTech, Croatia. doi: 10.5772/16004
37. Bellman R (1961) Adaptive control processes: a guided tour. Princeton University Press, New Jersey