| Application of cepstrum and neural network to bearing fault detection
Yean-Ren Hwang, Kuo-Kuang Jen, Yu-Ta Shen
The Journal of Mechanical Science and Technology, vol. 23, no. 10, pp.2730-2737, 2009
Abstract : This paper proposes an integrated system for motor bearing diagnosis that combines the cepstrum coefficient method
for feature extraction from motor vibration signals and artificial neural network (ANN) models. We divide the motor
vibration signal, obtain the corresponding cepstrum coefficients, and classify the motor systems through ANN models.
Utilizing the proposed method, one can identify the characteristics hiding inside a vibration signal and classify the signal,
as well as diagnose the abnormalities. To evaluate this method, several tests for the normal and abnormal conditions
were performed in the laboratory. The results show the effectiveness of cepstrum and ANN in detecting the bearing
condition. The proposed method successfully extracted the corresponding feature vectors, distinguished the difference,
and classified bearing faults correctly.
Keyword : Fault classification; Cepstrum; Machine condition monitoring (MCM); Artificial neural network |
