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

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  • Jianing Wu
  • Shaoze Yan

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.

Suggested Citation

  • Jianing Wu & Shaoze Yan, 2014. "An approach to system reliability prediction for mechanical equipment using fuzzy reasoning Petri net," Journal of Risk and Reliability, , vol. 228(1), pages 39-51, February.
    • Handle: RePEc:sae:risrel:v:228:y:2014:i:1:p:39-51
    DOI: 10.1177/1748006X13495130
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    3. Yu Liu & Hong-Zhong Huang & Dan Ling, 2013. "Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(3), pages 545-555.
    4. Castet, Jean-Francois & Saleh, Joseph H., 2009. "Satellite and satellite subsystems reliability: Statistical data analysis and modeling," Reliability Engineering and System Safety, Elsevier, vol. 94(11), pages 1718-1728.
    5. Murthy, D.N.P. & Rausand, M. & Virtanen, S., 2009. "Investment in new product reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(10), pages 1593-1600.
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