Automatic feature selection and failure diagnosis for bearing faults

Haw Ching Yang, Hao Tieng, Shih Fang Chen

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

This study develops a novel dual-stage diagnosis scheme for accelerating bearing failure diagnosis. The schema integrates the intelligent methods, i.e., genetic algorithm, k-nearest neighbors, and neural network, in the featuring and modeling stages to automatically select the significant features from various feature candidates for modeling bearing failure modes. After applying the scheme to classify two cases of bearing faults, the mean training time for model diagnosis is reduced to 8.1% that of using a neural network model. In this work, case 1 indicates that training and testing accuracies of seven failure modes are 98.8% and 94.5%, respectively; in addition, case 2 shows that the training and testing accuracies are 96.2% and 91.8% while using the top seven features.

原文English
主出版物標題SICE 2011 - SICE Annual Conference 2011, Final Program and Abstracts
發行者Society of Instrument and Control Engineers (SICE)
頁面235-239
頁數5
ISBN(列印)9784907764395
出版狀態Published - 2011 1月 1
事件50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011 - Tokyo, Japan
持續時間: 2011 9月 132011 9月 18

出版系列

名字Proceedings of the SICE Annual Conference

Other

Other50th Annual Conference on Society of Instrument and Control Engineers, SICE 2011
國家/地區Japan
城市Tokyo
期間11-09-1311-09-18

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 電腦科學應用
  • 電氣與電子工程

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