Applications of fault diagnosis in rotating machinery by using time series analysis with neural network

Expert Systems with Applications

Volumn 37, Issue 2, 2010, Pages 1696-1702

  Wang, Chun Chieh ^a   Kang, Yuan ^b   Shen, Ping Chen ^c   Chang, Yeon Pun ^b   Chung, Yu Liang ^a  

^a INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE   (Taiwan)

^b CHUNG YUAN CHRISTIAN UNIVERSITY   (Taiwan)

^c YUAN ZE UNIVERSITY   (Taiwan)

Author keywords

Autoregressive; Fault diagnosis; Machinery; Neural network; Time series analysis

Indexed keywords

AR MODELS; AUTO-REGRESSIVE; AUTOREGRESSIVE METHODS; BACK-PROPAGATION NEURAL NETWORKS; DIAGNOSIS METHODS; FAULT DIAGNOSIS; FAULT DIAGNOSIS METHOD; FAULT TYPES; MATH MODEL; SIGNAL FEATURES; TIME-DIFFERENCES; VIBRATION AMPLITUDE;

ARGON; FAILURE ANALYSIS; KALMAN FILTERS; MACHINERY; MATHEMATICAL MODELS; NEURAL NETWORKS; TIME SERIES; VIBRATION ANALYSIS;

TIME SERIES ANALYSIS;

EID: 71749111101     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.06.089     Document Type: Article

References (11)

1. Baillie D.C., and Mathew J. A comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing 10 1 (1966) 1-17
2. Endo, H., & Randall, R. B. (2007). Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter. Mechanical Systems and Signal Processing, Vol. 21 (pp. 906-919), No. 2.
3. Finley W.R., Hodowanec M.M., and Holter W.G. An analytical approach to solving motor vibration problems. IEEE Transactions on Industry Applications 36 5 (2000) 1467-1480
4. Güler I., Kara S., Güler N.F., and Kiymik M.K. Application of autoregressive and fast fourier transform spectral analysis to tricuspid and mitral valve stenosis. Computer Methods and Programs in Biomedicine 49 1 (1966) 29-36
5. Junsheng C., Dejie Y., and Yu Y. A fault diagnosis approach for roller bearings based on EMD method and AR model. Mechanical Systems and Signal Processing 20 2 (2006) 350-362
6. Kang Y., Wang C.C., Chang Y.P., Hsueh C.C., and Chang M.C. Certainty improvement in diagnosis of multiple faults by using versatile membership functions for fuzzy neural networks. Lecture Notes in Computer Science 3973 (2006) 370-375
7. Li B., Chow M.Y., Tipsuwan Y., and Hung J.C. Neural-network-based motor rolling bearing fault diagnosis. IEEE Transactions on Industrial Electronics 47 5 (2000) 1060-1069
8. Randall R.B. A new method of modeling gear faults. ASME Journal of Mechcanical Design 104 2 (1982) 259-267
9. Randall, R. B., & Hee, J. (1981). Cepstrum analysis. Technical review - Bruel & Kjaer English ed., no. 3 (pp. 3-44).
10. Sorsa T., Koivo H.N., and Koivisto H. Neural networks in process fault diagnosis. IEEE Transactions on Systems, Man and Cybernetics 21 4 (1991) 815-825
11. Wang W., and Wong A.K. Autoregressive model-based gear fault diagnosis. Journal of Vibration and Acoustics, Transactions of the ASME 124 2 (2002) 172-179