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Reliability analysis and functional design using Bayesian networks generated automatically by an “Idea Algebra†framework

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  • Amrin, Andas
  • Zarikas, Vasileios
  • Spitas, Christos

Abstract

Currently, the application of intelligent tools and decision methods in reliability, dependability, and maintenance analysis shows an increasing trend due to the complexity of the systems. The Bayesian Network (BN)-based methods are very efficient for this kind of analysis, but the process of constructing the BN is very routine and time-consuming and requires a lot of human effort. One good solution is to construct a proper BN model of a system, with the guidance of a semantic method. Thus, we introduce a novel methodology that automates the BN generation process for reliability analysis directly from the system's description. The method uses an engineering design representation technique to create a BN and allows to evaluate it automatically. The created GeNIe files can be edited and reused for further analysis which increases reusability of engineering design data. For the validation of the developed method, it was applied to an automotive powertrain system. Finally, the Bayesian Networks of 25 different automobiles were evaluated and tested with sensitivity analysis.

Suggested Citation

  • Amrin, Andas & Zarikas, Vasileios & Spitas, Christos, 2018. "Reliability analysis and functional design using Bayesian networks generated automatically by an “Idea Algebra†framework," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 211-225.
    • Handle: RePEc:eee:reensy:v:180:y:2018:i:c:p:211-225
    DOI: 10.1016/j.ress.2018.07.020
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    2. Shi, Yong & Zhang, Linzi, 2023. "Modelling long- and short-term multi-dimensional patterns in predictive maintenance with accumulative attention," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
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    5. Ji, Chenyi & Su, Xing & Qin, Zhongfu & Nawaz, Ahsan, 2022. "Probability Analysis of Construction Risk based on Noisy-or Gate Bayesian Networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

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