Application of cepstrum and neural network to bearing fault detection

Yean Ren Hwang, Kuo Kuang Jen, Yu Ta Shen

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


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.

Original languageEnglish
Pages (from-to)2730-2737
Number of pages8
JournalJournal of Mechanical Science and Technology
Issue number10
StatePublished - Oct 2009


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


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