基于时变模型辨识的高速列车复合故障诊断

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英文篇名：Time-varying model identified based coupled fault diagnosis for high speed trains 作者：张坤鹏 ; 姜斌 ; 陈复扬 ; 安春兰 ; 任锋 英文作者：ZHANG Kun-peng;JIANG Bin;CHEN Fu-yang;AN Chun-lan;REN Feng;College of Automation Engineering,Nanjing University of Aeronautics and Astronautics;School of Electrical and Automation Engineering,East China Jiaotong University;Lanzhou Electricity Depot; 关键词：高速列车 ; T-S时变模型辨识 ; 模糊聚类 ; 复合故障诊断 ; 稳定性分析 ; 报警等级 英文关键词：high speed train;;T-S time-varying model identification;;fuzzy clustering;;coupled fault diagnosis;;stability analysis;;alarm prioritization 中文刊名：KZYC 英文刊名：Control and Decision 机构：南京航空航天大学自动化学院;华东交通大学电气与自动化工程学院;兰州铁路局兰州电务段; 出版日期：2018-08-21 14:47 出版单位：控制与决策 年：2019 期：v.34 基金：国家自然科学基金项目(61490703);; 江西省教育厅项目(GJJ170412);; 江西省自然科学基金项目(20181BAB211018) 语种：中文; 页：KZYC201902007 页数：5 CN：02 ISSN：21-1124/TP 分类号：53-57

摘要

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.
        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 CRH5 G, the effectiveness of the proposed algorithm is verified.

引文

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