Extracting relevant features for diagnosing machine tool faults in cloud architecture

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2015 IEEE Conference on Automation Science and Engineering
Subtitle of host publicationAutomation for a Sustainable Future, CASE 2015
PublisherIEEE Computer Society
Pages1434-1439
Number of pages6
ISBN (Electronic)9781467381833
DOIs
Publication statusPublished - 2015 Oct 7
Event11th IEEE International Conference on Automation Science and Engineering, CASE 2015 - Gothenburg, Sweden
Duration: 2015 Aug 242015 Aug 28

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2015-October
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other11th IEEE International Conference on Automation Science and Engineering, CASE 2015
CountrySweden
CityGothenburg
Period15-08-2415-08-28

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

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