The system structure of train–ground wireless communication systems (TWCSs) is extremely complicated due to the use of fault tolerant technology to improve their performance. This complex structure raises several challenges in fault diagnosis for TWCSs, such as epistemic uncertainty, dynamic fault behaviors, and common cause failure (CCF). A fault diagnostic system is proposed to deal with these challenges based on Petri nets and gray relational analysis in this paper. Specifically, the fuzzy analytic hierarchy process is used to evaluate the failure data of components to handle epistemic uncertainty. Furthermore, the dynamic fault tree of TWCSs is established and converted into a generalized stochastic Petri net to calculate several reliability parameters used for fault diagnosis. Besides, a β factor model is employed to resolve the problem of CCF in TWCSs. In addition, Birnbaum importance measure (BIM), risk achievement worth (RAW) and test cost are considered comprehensively to obtain the optimal diagnostic sequence using an improved gray relational analysis. Finally, a numerical example is presented to demonstrate the efficiency of the proposed fault diagnostic system.

火车地面无线通信系统（TWCS）的系统结构非常复杂，这是因为使用了容错技术来提高其性能。这种复杂的结构在TWCS的故障诊断中提出了一些挑战，例如认知不确定性，动态故障行为和共因故障（CCF）。提出了一种基于Petri网和灰色关联分析的故障诊断系统来应对这些挑战。具体而言，模糊层次分析法用于评估组件的故障数据以处理认知不确定性。此外，建立了TWCS的动态故障树，并将其转换为广义的随机Petri网，以计算用于故障诊断的几个可靠性参数。除了，采用β因子模型来解决TWCS中的CCF问题。此外，综合考虑了伯恩鲍姆重要性度量（BIM），风险实现价值（RAW）和测试成本，以使用改进的灰色关联分析获得最佳诊断序列。最后，给出了一个数值例子来说明所提出的故障诊断系统的效率。