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Reliability analysis of hydraulic transmission oil supply system considering common cause failure and maintenance correlation with success oriented

  • Xinlei Wang
  • , Hongwei Zhang
  • , Zhe Wang
  • , Xiaojian Yi*
  • *此作品的通讯作者
  • Beijing Institute of Technology
  • Inner Mongolia First Machinery Group Co. Ltd.

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

This paper presents an approach for reliability analysis of repairable systems with two-unit parallel structure considering Common Cause Failure (CCF) and maintenance correlation based on GO methodology. First, the GO algorithm for dealing with CCF is introduced. Then, the common cause failure probability formulas of two-unit parallel structure considering maintenance correlation are deduced based on Markov theory. Furthermore, the analysis process of the new GO method is formulated. Finally, the dynamic availability analysis of HTOSS is conducted by the GO method. And the result is compared with the result of system considering CCF, and the result of system without considering CCF and maintenance correlation. The results show that the CCF and maintenance correlation are not ignored for reliability analysis of such system. All in all, this study not only widens the application of GO method. But it also provides guidance and an approach for reliability analysis of repairable systems with two-unit parallel structure considering CCF and maintenance correlation.

源语言英语
主期刊名Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
编辑Chuan Li, Shaohui Zhang, Jianyu Long, Diego Cabrera, Ping Ding
出版商Institute of Electrical and Electronics Engineers Inc.
435-441
页数7
ISBN(电子版)9781728101996
DOI
出版状态已出版 - 8月 2019
活动2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 - Beijing, 中国
期限: 15 8月 201917 8月 2019

丛书

姓名Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019

会议

会议2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
国家/地区中国
Beijing
时期15/08/1917/08/19

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