<p>Current research on the reliability of onboard equipment primarily focuses on natural degradation and common cause failure in hot standby structures, often neglecting the impact of periodic switching strategy on the reliability of cold standby systems. To address this gap, this study introduces the concept of virtual age into onboard subsystems to analyze the mechanism by which active switching strategy enhance unit reliability. Additionally, the influence of human operation on onboard equipment is considered. Building upon the assumption that the natural degradation lifespan of a unit follows a Weibull distribution, the reliability function of the unit is determined by accounting for multiple factors. The solution to the model is obtained using a Stochastic Petri Net-based Monte Carlo (SPN-MC) method, with the solution steps optimized for onboard subsystems. The results indicate that the lifespan of the onboard subsystem follows a Weibull distribution with a shape parameter of 3.34 and a scale parameter of 22,071.12. The active switching strategy for cold standby structure is shown to improve the operational reliability of onboard equipment. Among failure types, wireless timeout occurs most frequently. These findings provide valuable insights for enhancing the reliability of onboard equipment.</p>

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Reliability analysis of the train control onboard subsystem considering periodic switching of cold standby units

  • Jinping Qi,
  • Haohao Liu,
  • Yangyang Zhao

摘要

Current research on the reliability of onboard equipment primarily focuses on natural degradation and common cause failure in hot standby structures, often neglecting the impact of periodic switching strategy on the reliability of cold standby systems. To address this gap, this study introduces the concept of virtual age into onboard subsystems to analyze the mechanism by which active switching strategy enhance unit reliability. Additionally, the influence of human operation on onboard equipment is considered. Building upon the assumption that the natural degradation lifespan of a unit follows a Weibull distribution, the reliability function of the unit is determined by accounting for multiple factors. The solution to the model is obtained using a Stochastic Petri Net-based Monte Carlo (SPN-MC) method, with the solution steps optimized for onboard subsystems. The results indicate that the lifespan of the onboard subsystem follows a Weibull distribution with a shape parameter of 3.34 and a scale parameter of 22,071.12. The active switching strategy for cold standby structure is shown to improve the operational reliability of onboard equipment. Among failure types, wireless timeout occurs most frequently. These findings provide valuable insights for enhancing the reliability of onboard equipment.