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Online condition assessment of high-speed trains based on Bayesian forecasting approach and time series analysis

  • Zhang, Lin-Hao (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Wang, You-Wu (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Ni, Yi-Qing (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Lai, Siu-Kai (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
  • Received : 2017.12.20
  • Accepted : 2018.03.31
  • Published : 2018.05.25
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Abstract

High-speed rail (HSR) has been in operation and development in many countries worldwide. The explosive growth of HSR has posed great challenges for operation safety and ride comfort. Among various technological demands on high-speed trains, vibration is an inevitable problem caused by rail/wheel imperfections, vehicle dynamics, and aerodynamic instability. Ride comfort is a key factor in evaluating the operational performance of high-speed trains. In this study, online monitoring data have been acquired from an in-service high-speed train for condition assessment. The measured dynamic response signals at the floor level of a train cabin are processed by the Sperling operator, in which the ride comfort index sequence is used to identify the train's operation condition. In addition, a novel technique that incorporates salient features of Bayesian inference and time series analysis is proposed for outlier detection and change detection. The Bayesian forecasting approach enables the prediction of conditional probabilities. By integrating the Bayesian forecasting approach with time series analysis, one-step forecasting probability density functions (PDFs) can be obtained before proceeding to the next observation. The change detection is conducted by comparing the current model and the alternative model (whose mean value is shifted by a prescribed offset) to determine which one can well fit the actual observation. When the comparison results indicate that the alternative model performs better, then a potential change is detected. If the current observation is a potential outlier or change, Bayes factor and cumulative Bayes factor are derived for further identification. A significant change, if identified, implies that there is a great alteration in the train operation performance due to defects. In this study, two illustrative cases are provided to demonstrate the performance of the proposed method for condition assessment of high-speed trains.

Keywords

Acknowledgement

Supported by : Research Grants Council of the Hong Kong, Ministry of Science and Technology of China, National Rail Transit Electrification and Automation Engineering Technology Research Center, National Natural Science Foundation of China

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