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Manufacturing system evaluation in terms of system reliability via long short-term memory

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  • Huang, Cheng-Hao
  • Lin, Yi-Kuei

Abstract

With the development of system reliability evaluation, more and more studies are published by considering more attributes or adopting different calculation methods. However, existing deep learning (DL) approaches for predicting system reliability of a multi-state manufacturing network (MMN) only considering single attribute, such as time or machine failure. A more comprehensive consideration about an MMN is deliberated in this study. The Long Short-Term Memory is then utilized to construct the prediction model to process time series of an MMN data. Through the experimental results, the proposed prediction model outperforms than the existing method and other DL method in terms of root mean square error and mean absolute error. Moreover, the parameter of an MMN and hyperparameter of the proposed prediction model are discussed to investigate the optimized combination for system reliability prediction.

Suggested Citation

  • Huang, Cheng-Hao & Lin, Yi-Kuei, 2024. "Manufacturing system evaluation in terms of system reliability via long short-term memory," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:reensy:v:251:y:2024:i:c:s095183202400437x
    DOI: 10.1016/j.ress.2024.110365
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    1. Song, Zhiting & Zhu, Jianhua & Chen, Kun, 2025. "Robustness analysis of smart manufacturing systems against resource failures: A two-layered network perspective," Reliability Engineering and System Safety, Elsevier, vol. 253(C).

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