Research on fault time prediction method for high speed rail BTM unit based on multi method interactive validation

Abstract

The Balise Transmission Module (BTM) unit of the on-board train control system is a crucial component. Due to its unique installation position and complex environment, this unit has a higher fault rate within the on-board train control system. To conduct fault prediction for the BTM unit based on actual fault data, this study proposes a prediction method combining reliability statistics and machine learning, and achieves the fusion of prediction results from different dimensions through multi-method interactive validation. Firstly, a method for predicting equipment fault time targeting batch equipment is introduced. This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty, thereby predicting the remaining faultless operating probability of the BTM unit. Secondly, considering the complexity of the BTM unit’s fault mechanism, the small sample size of fault cases, and the potential presence of multiple fault features in fault text records, an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree (Bayes-GBRT) is proposed. This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms, with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment. Finally, a multi-method interactive validation approach is proposed, enabling the fusion and validation of multi-dimensional results. The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution, and the parameter estimation results are basically consistent, verifying the accuracy and effectiveness of the prediction results. The above research findings can provide technical support for the maintenance and modification of BTM units, effectively reducing maintenance costs and ensuring the safe operation of high-speed railway, thus having practical engineering value for preventive maintenance.

Keywords

High speed rail BTM unit / Remaining faultless operating time / Machine learning / Multi method interactive verification

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Limin Fu reports financial support was provided by Integrated Rail Transit Dispatch Control and Intermodal Transport Service Technology Project. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research was supported by the Integrated Rail Transit Dispatch Control and Intermodal Transport Service Technology Project (Grant No. 2022YFB4300500).

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