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

  • Tie-Jun Cui
  • Sha-sha Li
S.I. : Emergence in Human-like Intelligence towards Cyber-Physical Systems
  • 82 Downloads

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.

Keywords

Safety system engineering Space fault tree System reliability Multi-factor influence Deep learn 

Notes

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.

Compliance with ethical standards

Conflict of interest

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. All the authors listed have approved the manuscript that is enclosed.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.College of Safety Science and EngineeringLiaoning Technical UniversityFuxinChina
  2. 2.Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of EducationFuxinChina
  3. 3.Tunnel and Underground Structure Engineering Center of LiaoningDalian Jiaotong UniversityDalianChina

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