Extracting relevant features for diagnosing machine tool faults in cloud architecture

研究成果: Conference contribution

摘要

This paper presents a cloud diagnosis architecture to support diagnosis of different machine tool faults with similar abnormal events. Lacking the corresponding features of failure historical data, similar abnormal events are insufficient to be used for identifying the root causes of faults. On the basis of a novel event-oriented process monitoring and backtracking (EOPMB) method and the clustering non-dominated sorting genetic algorithm (CNSGA), this paper proposes a cloud diagnosis architecture for identifying failure causes by extracting relevant features of various faults from different machine tools. Results show that the proposed architecture can assist users in improving diagnosis performance.

原文English
主出版物標題2015 IEEE Conference on Automation Science and Engineering
主出版物子標題Automation for a Sustainable Future, CASE 2015
發行者IEEE Computer Society
頁面1434-1439
頁數6
ISBN(電子)9781467381833
DOIs
出版狀態Published - 2015 十月 7
事件11th IEEE International Conference on Automation Science and Engineering, CASE 2015 - Gothenburg, Sweden
持續時間: 2015 八月 242015 八月 28

出版系列

名字IEEE International Conference on Automation Science and Engineering
2015-October
ISSN(列印)2161-8070
ISSN(電子)2161-8089

Other

Other11th IEEE International Conference on Automation Science and Engineering, CASE 2015
國家Sweden
城市Gothenburg
期間15-08-2415-08-28

    指紋

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

引用此

Li, Y. Y., Yang, H. C., Tieng, H., & Cheng, F. T. (2015). Extracting relevant features for diagnosing machine tool faults in cloud architecture. 於 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015 (頁 1434-1439). [7294299] (IEEE International Conference on Automation Science and Engineering; 卷 2015-October). IEEE Computer Society. https://doi.org/10.1109/CoASE.2015.7294299