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Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network

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Abstract

The conventional troubleshooting methods for high-speed railway on-board equipment, with over-reliance on personnel experience, is characterized by one-sidedness and low efficiency. In the process of high-speed train operation, numerous text-based on-board logs are recorded by on-board computers. Machine learning methods can help technicians make a correct judgment of fault types using the on-board log reasonably. Therefore, a fault classification model of on-board equipment based on attention capsule networks is proposed. This paper presents an empirical exploration of the application of a capsule network with dynamic routing in fault classification. A capsule network can encode the internal spatial part-whole relationship between various entities to identify the fault types. As the importance of each word in the on-board log and the dependencies between them have a significant impact on fault classification, an attention mechanism is incorporated into the capsule network to distill important information. Considering the imbalanced distribution of normal data and fault data in the on-board log, the focal loss function is introduced into the model to adjust the imbalanced data. The experiments are conducted on the on-board log of a railway bureau and compared with other baseline models. The experimental results demonstrate that our model outperforms the compared baseline methods, proving the superiority and competitiveness of our model.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (No. 61763025), Gansu Science and Technology Program Project (No. 18JR3RA104), Industrial support program for colleges and universities in Gansu Province (No. 2020C-19), Lanzhou Science and Technology Project (No. 2019-4-49).

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Authors and Affiliations

  1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

    Lu-Jie Zhou, Jian-Wu Dang & Zhen-Hai Zhang

  2. Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic & Image Processing, Lanzhou, 730070, China

    Jian-Wu Dang

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  1. Lu-Jie Zhou
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Correspondence to Lu-Jie Zhou.

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Colored figures are available in the online version at https://link.springer.com/journal/11633

Lu-Jie Zhou received the B. Sc. degree in traffic information engineering & control from Lanzhou Jiaotong University, China in 2015. She is currently a Ph. D. degree candidate in traffic information engineering & control from Lanzhou Jiaotong University, China.

Her research interests include intelligent fault diagnosis and natural language processing.

Jian-Wu Dang received the Ph. D. degree in electrification & automation of railway traction from Southwest Jiaotong University, China in 1996. He is a professor, doctoral supervisor, vice president of Lanzhou Jiaotong University, China. He is a national candidate for the New Century Ten Million Talent Project and one of the first batch of special science and technology experts in Gansu Province. He is an expert with outstanding contributions from the Ministry of Railways and won the 6th Zhan Tianyou Railway Science and Technology Award. He has published five monographs and published more than 170 academic papers.

His research interests include intelligent information processing, intelligent transportation, and image processing.

Zhen-Hai Zhang received the Ph. D. degree in traffic information engineering & control from Lanzhou Jiaotong University, China in 2014. He is an associate professor, master supervisor of Lanzhou Jiaotong University, China. Now he is in charge of the National Natural Science Foundation, the postdoctoral fund of China, the Natural Science Foundation of Gansu Province. He has published 14 relevant academic papers and participated in the compilation of 2 teaching materials.

His research interest is intelligent transportation.

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Zhou, LJ., Dang, JW. & Zhang, ZH. Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network. Int. J. Autom. Comput. 18, 814–825 (2021). https://doi.org/10.1007/s11633-021-1291-2

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