The rail vehicle door system is one of the key components of rail vehicles. Its failure rate accounts for more than 30% of vehicle failures. By analyzing early warnings provided by subhealth data from the door system, the efficiency and reliability of their health maintenance can be effectively improved and stable operation of the door system can also be guaranteed. In this paper, early-stage resistance changes in the subhealth state of rail vehicle door systems are considered as the research object. Firstly, the distribution rules for the motor parameters are studied, and the time-domain and normal operating envelope features of the operating motor are extracted. Secondly, subhealth conditions with different resistances are simulated using a test rig, and the experimental data are applied to summarize the rules. According to the subhealth types and the distribution of features, diagnostic rules for subhealth are formulated. To check the possibility of fault diagnosis, a verification using running rail vehicle door system data is carried out in MATLAB. The results reveal that the misdiagnosis rate of resistance subhealth is 0% while the rate of missed diagnoses is 2%. Meanwhile, the diagnostic process based on the established rules is relatively efficient. This method is suitable for application for resistance subhealth diagnosis of urban rail vehicle door systems.

轨道车辆车门系统是轨道车辆的关键组件之一。故障率占车辆故障的30％以上。通过分析来自门系统的亚健康数据提供的预警，可以有效地提高其健康维护的效率和可靠性，还可以确保门系统的稳定运行。本文以轨道车辆车门系统亚健康状态下的早期阻力变化为研究对象。首先，研究了电动机参数的分布规律，提取了电动机的时域和正常工作包络特征。其次，使用试验台模拟了具有不同抵抗力的亚健康状况，并通过实验数据总结了规则。根据亚健康的类型和特征的分布，制定亚健康的诊断规则。为了检查故障诊断的可能性，在MATLAB中使用运行中的轨道车辆车门系统数据进行了验证。结果表明，耐药性亚健康的误诊率为0％，漏诊率为2％。同时，基于所建立规则的诊断过程相对有效。该方法适用于城市轨道车辆车门系统阻力亚健康诊断。结果表明，耐药性亚健康的误诊率为0％，漏诊率为2％。同时，基于所建立规则的诊断过程相对有效。该方法适用于城市轨道车辆车门系统阻力亚健康诊断。结果表明，耐药性亚健康的误诊率为0％，漏诊率为2％。同时，基于所建立规则的诊断过程相对有效。该方法适用于城市轨道车辆车门系统阻力亚健康诊断。