Automatic baseline-sample-selection scheme for baseline predictive maintenance

Chun Fang Chen, Yao Sheng Hsieh, Fan Tien Cheng, Hsien Cheng Huang, Saint Chi Wang

研究成果: Conference contribution

2 引文 斯高帕斯(Scopus)

摘要

A virtual-metrology-based (VM-based) baseline-predictive-maintenance (BPM) scheme was proposed by the authors recently. By applying the BPM scheme, fault diagnosis and prognosis can be accomplished and the requirement of massive historical failure data can also be released. The accuracy of the BPM scheme highly depends on the correctness of the baseline models in the BPM scheme. The samples of creating the target-device (TD) baseline model consist of the concise and healthy (C&H) historical samples and the fresh samples just after maintenance. Originally, each one of the C&H samples was checked manually to ensure that the sample was generated under healthy status and its data quality is good. However, this health-&-quality check process is so tedious and may also neglect deleting contradictory samples, which may deteriorate the BPM results and prohibit the usage of the BPM scheme. The purpose of this paper is to develop an automatic baseline-sample-selection (ABSS) scheme for selecting the C&H samples and deleting the contradictory samples automatically.

原文English
主出版物標題2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
頁面183-188
頁數6
DOIs
出版狀態Published - 2013 十二月 1
事件2013 IEEE International Conference on Automation Science and Engineering, CASE 2013 - Madison, WI, United States
持續時間: 2013 八月 172013 八月 20

出版系列

名字IEEE International Conference on Automation Science and Engineering
ISSN(列印)2161-8070
ISSN(電子)2161-8089

Other

Other2013 IEEE International Conference on Automation Science and Engineering, CASE 2013
國家/地區United States
城市Madison, WI
期間13-08-1713-08-20

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 電氣與電子工程

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