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Yang Liu

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Proceedings Article | 5 June 2024 Paper

Life prediction of railway relays based on XGboost-informer model

Jingjie Nie, Jing Xu, Chaojian Xing, Shuxin Liu, Yang Liu

Proceedings Volume 13163, 131634N (2024) https://doi.org/10.1117/12.3030154

KEYWORDS: Relays, Feature selection, Performance modeling, Education and training, Power supplies, Sensors, Photovoltaics, Feature extraction, Resistance, Decision trees

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Aiming at the problem of how to accurately and efficiently predict the life of railroad relays, a railroad relay electrical life prediction method integrating XGBoost and Informer is proposed. Firstly, a railroad DC relay electrical life test system platform is set up for testing. Various performance characteristic parameters are extracted from it, which characterize the operation of the relay. Secondly, the XGBoost feature selection method is used to perform data statute and screen the optimal feature parameters. Finally, the Informer model is utilized to learn the intra-sequence regularities and long-range dependencies between sequences to improve prediction accuracy and efficiency. A comparison example is established. The analysis of the example shows that the XGBoost-Informer model is obviously better than other single models. It has strong applicability and accuracy. For the state monitoring and fault diagnosis of the relay, it provides an effective way and another way of thinking.

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