An approach to system reliability prediction for mechanical equipment using fuzzy reasoning Petri net

Author:

Wu Jianing1,Yan Shaoze1

Affiliation:

1. State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, P.R. China

Abstract

Reliability prediction is a critical process of conceptual design for one evolutionary system when it has not been physically built. Due to the limitation of operation data, during the early stage of design, the reliability of a new product is difficult to predict, especially for complex mechanical systems. This article introduces a new method to predict reliability in the early stage of design phase, which uses fuzzy reasoning Petri net to generate the three values, representing complexity, importance and quality of subsystems for the product count reliability prediction. This approach solves the problems of data deficiency and high complexity in the existing methods. The effectiveness and advantages of the proposed method are validated by a case study of a real mechanical system of the spacecraft solar array. The analysis results show that this new method is able to reach an accurate predicted failure rate compared with the reported lifetime of the solar arrays.

Publisher

SAGE Publications

Subject

Safety, Risk, Reliability and Quality

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Knowledge representation and reasoning using fuzzy Petri nets: a literature review and bibliometric analysis;Artificial Intelligence Review;2022-11-23

2. Using expert judgement techniques to assess reliability for long service-life components: An application to railway wheelsets;Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability;2021-07-28

3. A bibliometric analysis of process system failure and reliability literature;Engineering Failure Analysis;2019-12

4. Reliability Evaluation for Mechanical Systems by Petri Nets;Petri Nets in Science and Engineering;2018-09-19

5. Availability models of series mechanical systems considering failure and maintenance dependencies;Transactions of the Canadian Society for Mechanical Engineering;2018-09-01

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