Hybrid Dynamic Probability-based Modeling Technique for Rolling Stock Failure Analysis

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The purpose of this study is to propose a novel hybrid dynamic probability-based failure analysis technique consisting of dynamic Bayesian discretization (DBD) and stochastic Petri nets (SPNs) for railway rolling stock (RS) failure analysis. Performing failure analysis and diagnoses for integrated RS subsystems is challenging and can lead to operational delays affecting fleet reliability and availability. This paper presents an integrated feature of updative adaptation using DBD methods to analyze prior continuous and discrete probability data—by means of evidence-based propagation to ascertain posterior faulty component states and simultaneously allowing for rapid failure notification, detection, and isolation of multiple RS subsystems using the reachability tree characteristics of SPNs. Unlike other dynamic probability methods, the DBD-SPN hybrid model presented here reduces computational time and enhances convergence accuracy using the Kullback–Leibler measure, sequential event analysis, and stable and low-entropy-error characteristics. In an extensive UK-based RS case study, it was observed that this approach is suitable for rapid failure notification, detection, and isolation of traction door interlock failure. It is also believed that the current study represents a useful contribution to the research and technology of hybrid DBD and SPNs for the failure analysis of a system consisting of multiple subsystems, since its application makes the difference between being able to evaluate realistically common cause and sequential failure analyses of complex systems.
Original languageEnglish
Pages (from-to)182376-182390
Number of pages15
JournalIEEE Access
Publication statusPublished - 2 Oct 2020


  • Adaptive updating
  • Aerodynamics
  • Analytical models
  • Bayes methods
  • Complex systems
  • Data models
  • Failure analysis
  • Reliability
  • dynamic Bayesian discretization
  • probability-based modeling
  • rolling stock
  • stochastic Petri nets


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