Volume 21 Issue 1
Aug.  2021
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(1): 338-357
MIAO Bing-rong, LIU Jun-li, ZHANG Ying, YANG Shu-wang, PENG Qi-ming, LUO Yao-xiang. Review on structural vibration damage identification technology for railway vehicles[J]. Journal of Traffic and Transportation Engineering, 2021, 21(1): 338-357. doi: 10.19818/j.cnki.1671-1637.2021.01.016
Citation: MIAO Bing-rong, LIU Jun-li, ZHANG Ying, YANG Shu-wang, PENG Qi-ming, LUO Yao-xiang. Review on structural vibration damage identification technology for railway vehicles[J]. Journal of Traffic and Transportation Engineering, 2021, 21(1): 338-357. doi: 10.19818/j.cnki.1671-1637.2021.01.016
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Review on structural vibration damage identification technology for railway vehicles

doi: 10.19818/j.cnki.1671-1637.2021.01.016

  • State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, Sichuan, China

Funds:

National Natural Science Foundation of China 51775456

Independent Subject of State Key Laboratory of Traction Power 2019TPL_T03

More Information
  • Author Bio:

    MIAO Bing-rong(1970-), male, associate professor, PhD, brmiao@home.swjtu.edu.cn

  • Received Date: 2020-09-27
  • Publish Date: 2021-08-27
    Abstract
  • 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.

     

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