A method of online anomaly perception and failure prediction for high-speed automatic train protection system. (October 2022)

• Record Type: Journal Article
• Authors: Kang, Renwei; Wang, Junfeng; Chen, Jianqiu; Zhou, Jingjing; Pang, Yanzhi; Guo, Longlong; Cheng, Jianfeng
• Is Part Of: Reliability engineering & system safety. Volume 226(2022)
• Publication Date: 2022-10
• Subjects: Anomaly perception -- Failure prediction -- Automatic train protection system -- Long short-term memory network -- Time series -- Intelligent operation and maintenance; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.108699
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: The temporal correlation of ATP operation logs is revealed based on LSTM network. Online anomaly perception of high-speed automatic train protection system. A fault prediction method of ATP system based on time series is proposed. There exists high-dimensional dependence between the evolution behavior of ATP failure rate time series and the historical data. ATP intelligent operation and maintenance data service platform is designed. 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.