Control of mechatronics systems: Ball bearing fault diagnosis using machine learning techniques

Hsuan Wen Peng, Pei Ju Chiang

研究成果: Conference contribution

10 引文 斯高帕斯(Scopus)

摘要

Ball bearing fault is one of the main causes of induction motor failure. This paper investigates in the fault diagnosis of ball bearing of three phase induction motor using random forest algorithm and C4.5 decision tree. The bearing conditions are classified to four categories: normal, bearing with inner race fault, bearing with ball fault and bearing with outer race fault. The statistical features used for classification are extracted from mechanical vibration signal in time domain and frequency domain. Principal component analysis (PCA) and linear discriminent analysis (LDA) are used to reduce the dimension and complexity of the feature set. The classification accuracy of random forest algorithm and C4.5 decision tree are analyzed and compared. The experimental results show that the random forest algorithm not only works better than the C4.5 decision tree but also can classify the ball bearing condition effectively.

原文English
主出版物標題ASCC 2011 - 8th Asian Control Conference - Final Program and Proceedings
頁面175-180
頁數6
出版狀態Published - 2011 八月 29
事件8th Asian Control Conference, ASCC 2011 - Kaohsiung, Taiwan
持續時間: 2011 五月 152011 五月 18

出版系列

名字ASCC 2011 - 8th Asian Control Conference - Final Program and Proceedings

Other

Other8th Asian Control Conference, ASCC 2011
國家Taiwan
城市Kaohsiung
期間11-05-1511-05-18

    指紋

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

引用此

Peng, H. W., & Chiang, P. J. (2011). Control of mechatronics systems: Ball bearing fault diagnosis using machine learning techniques. 於 ASCC 2011 - 8th Asian Control Conference - Final Program and Proceedings (頁 175-180). [5899067] (ASCC 2011 - 8th Asian Control Conference - Final Program and Proceedings).