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Reliability assessment using feed-forward neural network-based approximate meta-models

Author

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  • Zia-ur-Rehman Gondal
  • Jongsoo Lee

Abstract

This paper deals with an adaptation of artificial neural networks in the context of the reliability analysis of non-linear limit state functions. An extreme learning machine (ELM) that is categorized as a single-hidden-layer feed-forward neural network is considered in the present study. Using a trained ELM-based approximate meta-model, the reliability analysis is conducted in conjunction with Monte Carlo simulation. The ELM is compared with both single and multiple-hidden-layer back-propagation neural networks. A number of non-linear and large-dimensionality limit state functions are explored to support the proposed method in terms of approximation accuracy and reliability index.

Suggested Citation

  • Zia-ur-Rehman Gondal & Jongsoo Lee, 2012. "Reliability assessment using feed-forward neural network-based approximate meta-models," Journal of Risk and Reliability, , vol. 226(5), pages 448-454, October.
    • Handle: RePEc:sae:risrel:v:226:y:2012:i:5:p:448-454
    DOI: 10.1177/1748006X11433661
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    Cited by:

    1. Navneet Singh Bhangu & G. L. Pahuja & Rupinder Singh, 2017. "Enhancing reliability of thermal power plant by implementing RCM policy and developing reliability prediction model: a case study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1923-1936, November.
    2. 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.
    3. Cheng Lu & Yun-Wen Feng & Cheng-Wei Fei, 2019. "Weighted Regression-Based Extremum Response Surface Method for Structural Dynamic Fuzzy Reliability Analysis," Energies, MDPI, vol. 12(9), pages 1-16, April.
    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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