A method of online anomaly perception and failure prediction for high-speed automatic train protection system

Renwei Kang, Junfeng Wang, Jianqiu Chen, Jingjing Zhou, Yanzhi Pang, Longlong Guo and Jianfeng Cheng

Reliability Engineering and System Safety, 2022, vol. 226, issue C

Abstract: Automatic train protection (ATP) system is the key to ensure the safe operation of high-speed trains. However, the existing operation and maintenance mode for ATP systems cannot diagnose fault in time. In order to improve the protection capability of trains, this paper proposes an online anomaly perception and failure prediction method. First, with real-time operating data, an anomaly perception model based on long short-term memory network is established, where unstructured data are parsed into structured log keys and parameter vectors. It is trained with sequence matrices and its learning performance under different parameters is tested to find the optimal model. Experimental results show that the classification accuracy is 0.981, which is better than the existing methods. Then, with historical data, a failure prediction model based on time series is established, where one-dimensional time series of failure rate are reconstructed to high-dimensional space. The support vector regression method is used to fit the complex functional relationship between phase point and predicted point. And different algorithms are taken to find the optimal parameters. The results show that the model has the strongest generalization ability with the accuracy of 0.987. Finally, the intelligent operation and maintenance data service platform is designed.

Keywords: Anomaly perception; Failure prediction; Automatic train protection system; Long short-term memory network; Time series; Intelligent operation and maintenance (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:226:y:2022:i:c:s0951832022003246

DOI: 10.1016/j.ress.2022.108699

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