Train wheel degradation generation and prediction based on the time series generation adversarial network. Shangguan, A., Xie, G., Fei, R., Mu, L., & Hei, X. Reliability Engineering & System Safety, 229:108816, January, 2023. ![Train wheel degradation generation and prediction based on the time series generation adversarial network [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{shangguan_train_2023,
title = {Train wheel degradation generation and prediction based on the time series generation adversarial network},
volume = {229},
issn = {0951-8320},
url = {https://www.sciencedirect.com/science/article/pii/S0951832022004355},
doi = {10.1016/j.ress.2022.108816},
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.},
language = {en},
urldate = {2022-10-29},
journal = {Reliability Engineering \& System Safety},
author = {Shangguan, Anqi and Xie, Guo and Fei, Rong and Mu, Lingxia and Hei, Xinhong},
month = jan,
year = {2023},
keywords = {Data generation, Degradation analysis, Rail safety, Train wheel},
pages = {108816},
}
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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.","language":"en","urldate":"2022-10-29","journal":"Reliability Engineering & System Safety","author":[{"propositions":[],"lastnames":["Shangguan"],"firstnames":["Anqi"],"suffixes":[]},{"propositions":[],"lastnames":["Xie"],"firstnames":["Guo"],"suffixes":[]},{"propositions":[],"lastnames":["Fei"],"firstnames":["Rong"],"suffixes":[]},{"propositions":[],"lastnames":["Mu"],"firstnames":["Lingxia"],"suffixes":[]},{"propositions":[],"lastnames":["Hei"],"firstnames":["Xinhong"],"suffixes":[]}],"month":"January","year":"2023","keywords":"Data generation, Degradation analysis, Rail safety, Train wheel","pages":"108816","bibtex":"@article{shangguan_train_2023,\n\ttitle = {Train wheel degradation generation and prediction based on the time series generation adversarial network},\n\tvolume = {229},\n\tissn = {0951-8320},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0951832022004355},\n\tdoi = {10.1016/j.ress.2022.108816},\n\tabstract = {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. 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