轨道车辆结构振动损伤识别技术综述

从智能运维的角度阐述了利用结构振动损伤识别技术进行轨道车辆结构健康监测的重要性和必要性；根据不同损伤识别的适用范围，将结构振动损伤识别技术分为基于模型的方法和基于响应信号的方法；结合结构健康监测中损伤识别的不同层次，分析了以结构损伤的存在性、类型、定位和程度表征的不同识别方法；概括了轨道车辆运维过程中损伤识别技术的典型特征，讨论了基于模型的损伤识别中固有频率、模态形状、曲率模态等与模态参数有关方法的优缺点；分析了基于响应信号方法的应用现状和发展趋势，并阐述了模型修正和优化技术在结构损伤识别中的应用；重点分析了车辆关键部件故障诊断与监测中损伤识别技术的实施，讨论了结构振动损伤识别技术在未来轨道车辆智能运维策略中的主要发展方向，展望了未来轨道车辆部件的状态检修策略和智能运维技术。研究结果表明:轨道车辆的智能运维应该充分考虑结构振动损伤识别技术与人工智能等新技术的结合；大数据驱动的结构振动损伤识别技术能够更好解决车辆状态实时监测的技术难点；考虑复杂环境因素对轨道车辆结构部件损伤识别技术的影响，需要不断完善基于耦合振动效应的结构振动损伤识别技术及方法。 

Review on structural vibration damage identification technology for railway vehicles

The importance and necessity of using the structural vibration damage identification technology for monitoring the structural health of railway vehicles were described in the context of intelligent operation and maintenance. According to the application ranges of different damage identifications, the structural vibration damage identification technology was classified into the model-based and response signal-based methods. Different methods characterized by the existence, type, location, and severity of structural damage were analyzed based on different damage identification hierarchies in structural health monitoring. Typical characteristics of damage identification technology during the operation and maintenance of railway vehicles were briefly outlined. In addition, the strength and weakness of model-based damage identification with respect to its modal parameters such as the natural frequency, modal shape, and curvature mode shape were evaluated. The application states and development trends of response signal-based methods were analyzed, and the applications of model modification and optimization technology in the structural damage identification were also described. A detailed analysis was focused on the implementation of damage identification technology in the fault diagnosis and monitoring of vehicle key components, and the most prominent trend of structural vibration damage identification technology in the intelligent operation and maintenance of future railway vehicles was discussed. The state maintenance strategy and intelligent operation and maintenance technology for railway vehicle components were also prospected. Research result shows that the fusion of structural vibration damage identification technology with new methods, such as the artificial intelligence, should be thoroughly considered in the intelligent operation and maintenance of railway vehicles. The big data-driven structural vibration damage identification technology can better solve the technical difficulty in the real-time state monitoring of railway vehicles. It is necessary to further refine the structural vibration damage identification technology and method based on the coupled vibrational modes, owing to the effect of complex environmental factor on the damage identification technology for the railway vehicle structural components. 1 tab, 4 figs, 130 refs.