Bearing fault diagnosis based on multiscale permutation entropy and support vector machine

Shuen De Wu, Po Hung Wu, Chiu Wen Wu, Jian Jiun Ding, Chun Chieh Wang

Research output: Contribution to journalArticle

138 Citations (Scopus)


Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE) and multiscale entropy (MSE).

Original languageEnglish
Pages (from-to)1343-1356
Number of pages14
Issue number8
Publication statusPublished - 2012 Aug



  • Fault diagnosis
  • Machine vibration
  • Multiscale
  • Multiscale permutation entropy
  • Permutation entropy
  • Support vector machine

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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