Train wheel degradation generation and prediction based on the time series generation adversarial network. (January 2023)

• Record Type: Journal Article
• Authors: Shangguan, Anqi; Xie, Guo; Fei, Rong; Mu, Lingxia; Hei, Xinhong
• Is Part Of: Reliability engineering & system safety. Volume 229(2023)
• Publication Date: 2023-01
• Subjects: Rail safety -- Train wheel -- Degradation analysis -- Data generation; 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.2022.108816
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A new data dimension segmentation method is designed to segment the input dimension of the wheel wear degradation. A generation model of the wheel wear degradation based on the network and random process is constructed. A data-driven train wheel wear degradation prediction model is established. Two train wheel wear degradation generation and prediction examples are given. Abstract: To ensure the safe operation of high-speed railways, it is necessary to assess the reliability of its key components. Among them, as wheels are prone to wear degradation and the wear data acquisition process has the disadvantages of high cost and long cycle. There are few wheels degradation samples, which in turn makes the wheel degradation prediction have large errors. Hence, this paper uses the time series generator adversarial network (TimeGAN) to generate synthetic wheel degradation, in which the original data is segmented through a sliding window to obtain more input sets, and the noise distribution in the generator network is combined with the stationary gamma process (SGP). Then, the wheel degradation at measured distance k is predicted by the Gated Recurrent Unit (GRU) network. To evaluate the effectiveness of the proposed method, different methods in this paper are conducted for the experiment comparison. The experiment result shows that the proposed method has a better effect on the generation of train wheel degradation, and the Kullback-Leibler (KL) divergence and the prediction error are the smallest in the comparison. Hence, the proposed method can provide support for the further reliability analysis of railways and further ensure their operational safety.