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Volume 18 Issue 6
December  2021
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Lu-Jie Zhou, Jian-Wu Dang, Zhen-Hai Zhang. Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration. International Journal of Automation and Computing, vol. 18, no. 6, pp.935-946, 2021. https://doi.org/10.1007/s11633-021-1298-8
Citation: Lu-Jie Zhou, Jian-Wu Dang, Zhen-Hai Zhang. Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration. International Journal of Automation and Computing, vol. 18, no. 6, pp.935-946, 2021. https://doi.org/10.1007/s11633-021-1298-8
shu

Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration

doi: 10.1007/s11633-021-1298-8
  • 1.

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

  • 2.

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

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  • Author Bio:

    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. E-mail: 792321186@qq.com (Corresponding author) ORCID: 0000-0003-4808-6942

    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 5 monographs and published more than 170 academic papers. His research interests include intelligent information processing, intelligent transportation, and image processing. E-mail: dangjw@mail.lzjtu.cn

    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. He has published 14 relevant academic papers and participated in the compilation of 2 teaching materials. His research interest is intelligent transportation. E-mail: zhangzhenhai@lzjtu.cn

  • Received Date: 2020-10-07
  • Accepted Date: 2021-03-29
  • Publish Online: 2021-04-26
  • Publish Date: 2021-12-01
    Abstract
  • It is of great significance to guarantee the efficient statistics of high-speed railway on-board equipment fault information, which also improves the efficiency of fault analysis. Considering this background, this paper presents an empirical exploration of named entity recognition (NER) of on-board equipment fault information. Based on the historical fault records of on-board equipment, a fault information recognition model based on multi-neural network collaboration is proposed. First, considering Chinese recorded data characteristics, a method of constructing semantic features and additional features based on character granularity is proposed. Then, the two feature representations are concatenated and passed into the gated convolutional layer to extract the dependencies from multiple different subspaces and adjacent characters in parallel. Next, the local features are transmitted to the bidirectional long short-term memory (BiLSTM) to learn long-term dependency information. On top of BiLSTM, the sequential conditional random field (CRF) is used to jointly decode the optimized tag sequence of the whole sentence. The model is tested and compared with other representative baseline models. The results show that the proposed model not only considers the language characteristics of on-board fault records, but also has obvious advantages on the performance of fault information recognition.

     

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