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

研究成果: 雜誌貢獻期刊論文同行評審

182 引文 斯高帕斯(Scopus)

摘要

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).

原文英語
頁(從 - 到)1343-1356
頁數14
期刊Entropy
14
發行號8
DOIs
出版狀態已發佈 - 2012 八月

ASJC Scopus subject areas

  • 物理與天文學 (全部)

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