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Author:

Gao, Li-Xin (Gao, Li-Xin.) | Zhang, Jian-Yu (Zhang, Jian-Yu.) | Xu, Yong-Gang (Xu, Yong-Gang.) | Peng, Shi-Chun (Peng, Shi-Chun.) | Ren, Zhi-Qiang (Ren, Zhi-Qiang.)

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EI Scopus PKU CSCD

Abstract:

Due to the difficulty of knowledge accessing in traditional gear-box intelligent diagnosis system, a diagnosis technology based on CBR (Case-based Reasoning) and RBR (Rule-based Reasoning) hybrid method was proposed in this paper. On the basis of analyzing the advantages and disadvantages of the two methods, the combination of the reasoning technologies was utilized in fault diagnosis of gearbox of rolling mills. As a result, the accuracy and efficiency of fault diagnosis were improved greatly. Due to the shortage in similarity algorithm in traditional retrieval, a new case retrieval algorithm was proposed. Therefore, the problem of inaccuracy in traditional similarity algorithm was solved effectively.

Keyword:

Rolling mills Fault detection Failure analysis Case based reasoning Gears

Author Community:

  • [ 1 ] [Gao, Li-Xin]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
  • [ 2 ] [Zhang, Jian-Yu]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
  • [ 3 ] [Xu, Yong-Gang]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
  • [ 4 ] [Peng, Shi-Chun]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
  • [ 5 ] [Ren, Zhi-Qiang]Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China

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Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2010

Issue: 9

Volume: 36

Page: 1174-1180

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 12

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