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. 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. https://doi.org/10.1007/s11633-021-1298-8

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

doi: 10.1007/s11633-021-1298-8
More Information
  • 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
  • Available Online: 2021-04-26
  • 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.

     

  • loading
  • [1]
    J. Tekli. An overview on XML semantic disambiguation from unstructured text to semi-structured data: Background, applications, and ongoing challenges. IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 6, pp. 1383–1407, 2016. DOI: 10.1109/TKDE.2016.2525768.
    [2]
    X. F. Mu, W. Wang, A. P. Xu. Incorporating token-level dictionary feature into neural model for named entity recognition. Neurocomputing, vol. 375, pp. 43–50, 2020. DOI: 10.1016/j.neucom.2019.09.005.
    [3]
    F. Li, M. S. Zhang, B. Tian, B. Chen, G. H. Fu, D. H. Ji. Recognizing irregular entities in biomedical text via deep neural networks. Pattern Recognition Letters, vol. 105, pp. 105–113, 2018. DOI: 10.1016/j.patrec.2017.06.009.
    [4]
    X. Z. Yin, H. Zhao, J. B. Zhao, W. W. Yao, Z. L. Huang. Multi-neural network collaboration for Chinese military named entity recognition. Journal of Tsinghua University (Science and Technology), vol. 60, no. 8, pp. 648–655, 2020. DOI: 10.16511/j.cnki.qhdxxb.2020.25.004. (in Chinese)
    [5]
    L. Luo, Z. H. Yang, P. Yang, Y. Zhang, L. Wang, H. F. Lin, J. Wang. An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition. Bioinformatics, vol. 34, no. 8, pp. 1381–1388, 2018. DOI: 10.1093/bioinformatics/btx761.
    [6]
    D. M. Li, Y. Zhang, D. Y. Li, D. Q. Lin. Review of entity relation extraction methods. Journal of Computer Research and Development, vol. 57, no. 7, pp. 1424–1448, 2020. DOI: 10.7544/issn1000-1239.2020.20190358. (in Chinese)
    [7]
    R. Bharathi, R. Selvarani. Hidden Markov model approach for software reliability estimation with logic error. International Journal of Automation and Computing, vol. 17, no. 2, pp. 305–320, 2020. DOI: 10.1007/s11633-019-1214-7.
    [8]
    Z. Chen, H. Ji. Language specific issue and feature exploration in Chinese event extraction. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, ACM, Boulder, USA, pp. 209−212, 2009. DOI: 10.3115/1620853.1620910.
    [9]
    G. Luo, X. J. Huang, C. Y. Lin, Z. Q. Nie. Joint entity recognition and disambiguation. In Proceedings of Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, pp. 879−888, 2015.
    [10]
    Z. H. Zheng, W. B. Wu, X. Chen, R. X. Hu, X. Liu, P. Wang. A Traffic sensing and analyzing system using social media data. Acta Automatica Sinica, vol. 44, no. 4, pp. 656–666, 2018. DOI: 10.16383/j.aas.2017.c160537. (in Chinese)
    [11]
    R. F. He, S. Y. Duan. Joint Chinese event extraction based multi-task learning. Journal of Software, vol. 30, no. 4, pp. 1015–1030, 2019. DOI: 10.13328/j.cnki.jos.005380. (in Chinese)
    [12]
    X. Z. Ma, F. Xia. Unsupervised dependency parsing with transferring distribution via parallel guidance and entropy regularization. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, USA, pp. 1337−1348, 2014.
    [13]
    Y. M. Han, N. Ding, Z. Q. Geng, Z. Wang, C. Chu. An optimized long short-term memory network based fault diagnosis model for chemical processes. Journal of Process Control, vol. 92, pp. 161–168, 2020. DOI: 10.1016/j.jprocont.2020.06.005.
    [14]
    X. Hu, Y. M. Han, B. Yu, Z. Q. Geng, J. Z. Fan. Novel leakage detection and water loss management of urban water supply network using multiscale neural networks. Journal of Cleaner Production, vol. 278, Article number 123611, 2021. DOI: 10.1016/j.jclepro.2020.123611.
    [15]
    R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, P. Kuksa. Natural language processing (almost) from scratch. Journal of Machine Learning Research, vol. 12, pp. 2493–2537, 2011.
    [16]
    Z. H. Huang, W. Xu, K. Yu. Bidirectional LSTM-CRF models for sequence tagging, [Online], Available: https://arxiv.org/abs/1508.01991, Aug 9, 2015.
    [17]
    J. P. C. Chiu, E. Nichols. Named entity recognition with bidirectional LSTM-CNNs. Transactions of the Association for Computational Linguistics, vol. 4, pp. 357–370, 2016. DOI: 10.1162/tacl_a_00104.
    [18]
    Z. G. Liu, X. R. Chen. Research on relation extraction of named entity on social media in smart cities. Soft Computing, vol. 24, no. 15, pp. 11135–11147, 2020. DOI: 10.1007/s00500-020-04742-w.
    [19]
    X. Y. Li, Y. X. Meng, X. F. Sun, Q. H. Han, A. Yuan, J. W. Li. Is word segmentation necessary for deep learning of Chinese representations? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL, Florence, Italy, pp. 3242−3252, 2019. DOI: 10.18653/v1/P19-1314.
    [20]
    Q. Zhao, D. Wang, S. S. Xu, X. T. Zhang, X. X. Wang. A weakly supervised Chinese medical named entity recognition method based on RNN. Journal of Harbin Engineering University, 2020. (in Chinese)
    [21]
    L. Ratinov, D. Roth. Design challenges and misconceptions in named entity recognition. In Proceedings of the 13th Conference on Computational Natural Language Learning, ACM, Boulder, USA, pp. 147−155, 2009.
    [22]
    J. Wang. M. Wang. P. P. Li, L. Q. Liu, Z. Q. Zhao, X. G. Hu, X. D. Wu. Online feature selection with group structure analysis. IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 11, pp. 3029–3041, 2015. DOI: 10.1109/TKDE.2015.2441716.
    [23]
    H. Reddy, N. Raj, M. Gala, A. Basava. Text-mining-based fake news detection using ensemble methods. International Journal of Automation and Computing, vol. 17, no. 2, pp. 210–221, 2020. DOI: 10.1007/s11633-019-1216-5.
    [24]
    Y. Bengio, H. Schwenk, J. S. Senécal, F. Morin, J. L. Gauvain. Neural probabilistic language models. Innovations in Machine Learning, D. E. Holmes, L. C. Jain, Ed., Berlin, Heidelberg: Springer, pp. 137−186, 2006. DOI: 10.1007/3-540-33486-6_6.
    [25]
    T. Mikolov, I. Sutskever, K. Chen, G. Corrado, J. Dean. Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe USA, pp. 3111−3119, 2013.
    [26]
    L. C. Li, Z. Y. Wu, M. X. Xu, H. Meng, L. H. Cai. Combining CNN and BLSTM to extract textual and acoustic features for recognizing stances in mandarin ideological debate competition. In Proceedings of the Interspeech 2016, San Francisco, USA, pp. 1392−1396, 2016.
    [27]
    X. L. Tang, W. X. Lin, Y. M. Du, T. Wang. Short text feature extraction and classification based on serial-parallel convolutional gated recurrent neural network. Advanced Engineering Sciences, vol. 51, no. 4, pp. 125–132, 2019. DOI: 10.15961/j.jsuese.201801160. (in Chinese)
    [28]
    Y. L. Jin, J. F. Xie, W. S. Guo, C. Luo, D. J. Wu, R. Wang. LSTM-CRF neural network with gated self attention for Chinese NER. IEEE Access, vol. 7, pp. 136694–136703, 2019. DOI: 10.1109/ACCESS.2019.2942433.
    [29]
    Y. N. Dauphin, A. Fan, M. Auli, D. Grangier. Language modeling with gated convolutional networks, [Online], Available: https://arxiv.org/abs/1612.08083v3, 2017.
    [30]
    X. Glorot, Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. Journal of Machine Learning Research, vol. 9, pp. 249–256, 2010.
    [31]
    R. Pascanu, T. Mikolov, Y. Bengio. On the difficulty of training Recurrent Neural Networks. In Proceedings of the 30th International Conference on Machine Learning, Atlanta, USA, pp. 1310−1318, 2013.
    [32]
    S. Hochreiter, J. Schmidhuber. Long short-term memory. Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. DOI: 10.1162/neco.1997.9.8.1735.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(9)

    Article Metrics

    Article views (62) PDF downloads(11) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return