In situ health monitoring for bogie systems of CRH380 train on Beijing-Shanghai high-speed railway

Ming Hong, Qiang Wang, Zhongqing Su, Li Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

69 Citations (Scopus)


Based on the authors' research efforts over the years, an in situ structural health monitoring (SHM) technique taking advantage of guided elastic waves has been developed and deployed via an online diagnosis system. The technique and the system were recently implemented on China's latest high-speed train (CRH380CL) operated on Beijing-Shanghai High-Speed Railway. The system incorporated modularized components including active sensor network, active wave generation, multi-channel data acquisition, signal processing, data fusion, and results presentation. The sensor network, inspired by a new concept - "decentralized standard sensing", was integrated into the bogie frames during the final assembly of CRH380CL, to generate and acquire bogie-guided ultrasonic waves, from which a wide array of signal features were extracted. Fusion of signal features through a diagnostic imaging algorithm led to a graphic illustration of the overall health state of the bogie in a real-time and intuitive manner. The in situ experimentation covered a variety of high-speed train operation events including startup, acceleration/deceleration, full-speed operation (300 km/h), emergency braking, track change, as well as full stop. Mock-up damage affixed to the bogie was identified quantitatively and visualized in images. This in situ testing has demonstrated the feasibility, effectiveness, sensitivity, and reliability of the developed SHM technique and the system towards real-world applications.
Original languageEnglish
Pages (from-to)378-395
Number of pages18
JournalMechanical Systems and Signal Processing
Issue number2
Publication statusPublished - 4 Apr 2014


  • Beijing-Shanghai High-speed Railway
  • CRH380CL
  • Guided-wave-based damage detection
  • High-speed train bogie system
  • Signal processing
  • Structural health monitoring

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications


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