High-Speed Rail Suspension System Health Monitoring Using Multi-Location Vibration Data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
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Author(s)

  • Weiran Yao
  • Yang Zhao
  • Cai Yi
  • Jianhui Lin
  • Kwok Leung Tsui

Detail(s)

Original languageEnglish
Pages (from-to)2943-2955
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume21
Issue number7
Online published20 Jun 2019
Publication statusPublished - Jul 2020

Abstract

A novel data-driven framework to monitor the health status of high-speed rail suspension system by measuring train vibrations is proposed herein. Unlike existing methods, this framework does not rely on sophisticated dynamic models or high-fidelity simulations; it combines the power of data and domain knowledge to generate a model that can be trained quickly and adapted easily to different rail systems. In addition, the framework includes a module to generate a training dataset, tackling a typical challenge in real-world system monitoring, namely, the lack of labeled data due to practical limits. Based on the multi-output support vector regression (MSVR), the proposed framework can monitor the stiffness and damping coefficients of the suspension system using vibration signals measured on trains in real time. The framework comprises three modules. First, a simple suspension system dynamics model is built to generate a training dataset. Furthermore, key features are extracted from frequency response curves to reflect the impact of spring and damper degradation. Subsequently, a supervised learning model based on the MSVR is built to predict the stiffness and damping coefficients of suspension systems from features extracted in the second module. Once the model is built, real-time monitoring can be achieved by feeding the vibration signals as they are collected during operations. The proposed framework was evaluated on simulation data for its accuracy and tested on real-world operational data for its practicability.

Research Area(s)

  • Monitoring, Data models, Suspensions (mechanical systems), Vibrations, Feature extraction, Vehicle dynamics, Rail transportation, Health monitoring, suspension system, high-speed rail, multi-output support vector regression, vibration signal, field data, data-driven approach

Citation Format(s)

High-Speed Rail Suspension System Health Monitoring Using Multi-Location Vibration Data. / Hong, Ning; Li, Lishuai; Yao, Weiran; Zhao, Yang; Yi, Cai; Lin, Jianhui; Tsui, Kwok Leung.

In: IEEE Transactions on Intelligent Transportation Systems, Vol. 21, No. 7, 07.2020, p. 2943-2955.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review