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Deep learning of system reliability under multi-factor influence based on space fault tree

  • S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
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Abstract

For the fault tree analysis, a basic event probability is often complicated. The probability is not constant and even can be represented by function. In order to analyze the system reliability and related characteristics, we represent the probabilities of the basic events by functions. The variables of the function are n influencing factors on the basic events. We extend the top event probability from the constant value to n + 1-dimensional space considering n influencing factors, and the probability is n + 1th dimension. Further research the n + 1-dimensional space with related mathematical methods, and then, transform the system probability analysis into the problem of mathematic. The above ideas are the space fault tree (SFT). In SFT, component fault probability distribution replace basic event probability and system fault probability distribution replace top event probability. In this paper, we research the electrical system fault probability distribution and explain the related construction process. The main factors influencing the system are working temperature c and working time t. This paper constructs the three-dimensional fault probability distribution of the components and the system, and the probability importance and criticality importance of the components. With partial derivation of the system fault probability distribution by the c and t, we study the change trend of the fault probability. The optimal replacement schemes of components and the scheme considering the cost are obtained. The results show SFT is feasible and reasonable to analyze the fault probability of system under multi-factor influence and suitable for deep learning of the characteristics of the system reliability change.

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Acknowledgements

The author wishes to thank all his friends for their valuable critics, comments and assistances on this paper. This study was partially supported by the grants (Grant Nos. 51704141, 51674127, 51474121) from the Natural Science Foundation of China.

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Authors and Affiliations

  1. College of Safety Science and Engineering, Liaoning Technical University, Fuxin, 123000, China

    Tie-Jun Cui & Sha-sha Li

  2. Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education, Fuxin, 123000, China

    Tie-Jun Cui & Sha-sha Li

  3. Tunnel and Underground Structure Engineering Center of Liaoning, Dalian Jiaotong University, Dalian, 116028, China

    Tie-Jun Cui

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  1. Tie-Jun Cui
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Correspondence to Tie-Jun Cui.

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Cui, TJ., Li, Ss. Deep learning of system reliability under multi-factor influence based on space fault tree. Neural Comput & Applic 31, 4761–4776 (2019). https://doi.org/10.1007/s00521-018-3416-2

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  • DOI: https://doi.org/10.1007/s00521-018-3416-2

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