Application of cepstrum and neural network to bearing fault detection

Journal of Mechanical Science and Technology

Volumn 23, Issue 10, 2009, Pages 2730-2737

  Hwang, Yean Ren ^a   Jen, Kuo Kuang ^b   Shen, Yu Ta ^a  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b CHUNG SHAN INSTITUTE OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Artificial neural network; Cepstrum; Fault classification; Machine condition monitoring (MCM)

Indexed keywords

ABNORMAL CONDITIONS; ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK MODELS; BEARING CONDITIONS; BEARING FAULT; BEARING FAULT DETECTION; CEPSTRUM; COEFFICIENT METHODS; FAULT CLASSIFICATION; FEATURE VECTORS; INTEGRATED SYSTEMS; MACHINE CONDITION MONITORING (MCM); MOTOR BEARINGS; MOTOR SYSTEMS; VIBRATION SIGNAL;

BACKPROPAGATION; BEARINGS (MACHINE PARTS); BEARINGS (STRUCTURAL); FAULT DETECTION; FEATURE EXTRACTION; INTEGRATED OPTICS; MOTORS; NEURAL NETWORKS; SIGNAL DETECTION;

CONDITION MONITORING;

EID: 70350447155     PISSN: 1738494X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s12206-009-0802-9     Document Type: Article

References (13)

1. J. Zarei and J. Poshtan, Bearing fault detection using wavelet packet transform of induction motor stator current, Tribology International 40(5) (2007) 763-769.
2. R. B. W. Heng and M. J. M. Nor, Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition, Applied Acoustics 53(1-3) (1998) 211-226.
3. B. Li, M. Y. Chow, Y. Tipsuwan and J. C. Hung, Neural-network-based motor rolling bearing fault diagnosis, IEEE Transaction on Instrumentation Measurement 47(5) (2000) 1060-1069.
4. Z. Ye, B. Wu and A. R. Sadeghiam, Induction motor mechanical fault online diagnosis with the application of artificial neural network, Proc. IEEE Applied Power Electronics Conference and Exposition-APEC 2 (2001) 1015-1021.
5. G. Goddu, B. Li, M. Y. Chow and J. C. Hung, Motor bearing fault diagnosis by fundamental frequency amplitude based fuzzy decision system, Proc. of PIECON, 4 (1998), 1961-1965.
6. L. Eren and M. J. Devaney, Bearing damage detection via wavelet packet decomposition of the stator current, IEEE transaction on instrument and measurement 53(2) (2004) 431-436.
7. J. Cheng, D. Yu and Y. Yang, A fault diagnosis approach for roller bearings based on EMD method and AR model, Mechanical Systems and Signal Processing 20(2) (2006) 350-362.
8. D. Yu, J. Cheng and Y. Yang, Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings, Mechanical Systems and Signal Processing 19(2) (2005) 259-270.
9. B. Bogert, M. Healy and J. Tukey, The frequency analysis of time series for echoes, Proc. of Symp. on Time Series Analysis, M. Rosenblatt, Ed., Ch. 15, New York: J. Wiley (1963).
10. B. S. Atal, Effectiveness of linear prediction characteristics of the speech wave for automatic speaker identification and verification, J. Acoust. Soc. Amer., 55(6) (1974) 1304-1312.
11. A. Shufeng and L. Hui, Application of order Cepstrum and Neural Network to Gear Fault Detection, Proc. of IMACS (CESA), Beijing, China (2006) 1822-1827.
12. A. K. Jain and J. Mao, Special issue on artificial neural networks and statistical pattern recognition, IEEE Transaction on Neural Networks 8 (1997) 35-41.
13. J. D. Markel and A. Makhoul, Linear Prediction of Speech. New York: Springer-Verlag (1976).