基于时变模型辨识的高速列车复合故障诊断

高速列车信息控制系统因运行条件异常变化或操作不当会造成电机警告级高温、电机电流异常变化、电机转子断条以及气隙偏心等运行故障.这些随机发生的复合故障会影响速度等级和牵引力/制动力的调节,且难以采用基于单故障诊断方法建模故障与速度的关系,以及诊断报警等级.对此,提出一种基于Takagi-Sugeno(T-S)时变模型辨识的高速列车复合故障诊断方法.首先,采用多元统计检测指标离线辨识故障阈值并建立复合故障时变模型;然后,借助模糊聚类算法辨识故障特征值集合,利用模糊加权最小二乘法在线估计故障幅值并进行参数收敛性分析.最后,设计故障分离机制以刻画不同故障模式的报警等级并给出稳定性分析.基于CRH5G型高速列车实际运行数据的仿真结果验证了所提出方法的有效性.

Time-varying model identified based coupled fault diagnosis for high speed trains

Coupled fault of the high speed train information control system(HSTCS) can appear, when operational conditions change abnormally or operators do not react properly or timely. Typically multiple fault ambiguity groups, including motor warnings with over-temperature, over-current, bar broken fault and air gap eccenticity fault, are highly related to speed level and traction/barking forces regulation. However, it is difficult to apply single fault diagnosis based methods to model the relations between fault and speed or alarm prioritization. In this paper, a T-S time-varying model identified based diagnosis scheme is developed for the HSTCS. Firstly, a multivariate detection index is proposed to identify the thresholds off-line and then coupled fault time-varying model is built. Then, the optimal fuzzy model structure and fault characteristics set are established using the clustering algorithm. Then, a fuzzy weighted least square algorithm with parameters convergence is proposed to estimate the coupled fault. Meanwhile, fault isolation techniques with stability analysis are used to provide a clear alarm priority for different fault modes. Finally, through a coupled fault diagnosis experiments using real data of CRH5G, the effectiveness of the proposed algorithm is verified.