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Reliability prediction for evolutionary product in the conceptual design phase using neural network-based fuzzy synthetic assessment

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  • Yu Liu
  • Hong-Zhong Huang
  • Dan Ling

Abstract

Reliability prediction plays an important role in product lifecycle management. It has been used to assess various reliability indices (such as reliability, availability and mean time to failure) before a new product is physically built and/or put into use. In this article, a novel approach is proposed to facilitate reliability prediction for evolutionary products during their early design stages. Due to the lack of sufficient data in the conceptual design phase, reliability prediction is not a straightforward task. Taking account of the information from existing similar products and knowledge from domain experts, a neural network-based fuzzy synthetic assessment (FSA) approach is proposed to predict the reliability indices that a new evolutionary product could achieve. The proposed approach takes advantage of the capability of the back-propagation neural network in terms of constructing highly non-linear functional relationship and combines both the data sets from existing similar products and subjective knowledge from domain experts. It is able to reach a more accurate prediction than the conventional FSA method reported in the literature. The effectiveness and advantages of the proposed method are demonstrated via a case study of the fuel injection pump and a comparative study.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tsysxx:v:44:y:2013:i:3:p:545-555
    DOI: 10.1080/00207721.2011.617887
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    Cited by:

    1. He, Yuan & Meng, Zhiyi & Xu, Hong & Zou, Yue, 2020. "A dynamic model of evaluating differential automatic method for solving plane problems based on BP neural network algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 556(C).
    2. 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.
    3. Dai, Hongzhe & Zhang, Boyi & Wang, Wei, 2015. "A multiwavelet support vector regression method for efficient reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 132-139.
    4. Fink, Olga & Zio, Enrico & Weidmann, Ulrich, 2014. "Predicting component reliability and level of degradation with complex-valued neural networks," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 198-206.

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