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Reliability model for key components of urban rail transit train based on improved hunter-prey optimization

Author

Listed:
  • Jiecheng Zhong
  • Deqiang He
  • Zhenzhen Jin
  • Haimeng Sun
  • Sheng Shan

Abstract

The reliability of key components of urban rail transit (URT) plays an important role in the maintenance plans of URT. It is necessary to establish the reliability model of URT trains. In the current research, the reliability model has a limited scope of application and fails to accurately depict the reliability of key components in URT trains. To solve the above problem, a multi-peak type mixture Weibull distribution model is established using several three-parameter Weibull distributions based on fault modes of components sourced from historical lifetime data. Due to the complexity of this model, parameter estimation is challenging. For this purpose, an improved hunter-prey optimization (IHPO) was proposed to improve parameter estimation accuracy. Firstly, an improved Hénon chaos map was introduced to improve the distribution of the initial population. Secondly, the Lévy flight was introduced to increase the probability of the individual spreading to the whole range at the late stage. Lastly, a nonlinear balance factor was proposed to enhance the algorithm’s global search capability. The simulation experiment was carried out with examples of the balanced pressing wheel and the wheelset. The IHPO algorithm-based parameter estimation method shows the highest R -square with values of 0.996 and 0.999, respectively, and the lowest root mean square error with values of 0.019 and 0.008, respectively. The simulation results demonstrate that the stability and optimization of the HPO are improved, and the multi-peak mixture Weibull distribution model based on the IHPO can accurately depict URT trains’ reliability.

Suggested Citation

  • Jiecheng Zhong & Deqiang He & Zhenzhen Jin & Haimeng Sun & Sheng Shan, 2025. "Reliability model for key components of urban rail transit train based on improved hunter-prey optimization," Journal of Risk and Reliability, , vol. 239(2), pages 371-388, April.
    • Handle: RePEc:sae:risrel:v:239:y:2025:i:2:p:371-388
    DOI: 10.1177/1748006X241231067
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    References listed on IDEAS

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