Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration
文献类型：期刊论文
| 作者 | Lu-Jie Zhou2 |
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2021 |
| 卷号 | 18期号:6页码:935-946 |
| 关键词 | Train control system Chinese named entity recognition (NER) character feature gating mechanism bidirectional long short-term memory (BiLSTM) |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-021-1298-8 |
| 英文摘要 | 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46100]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic & Image Processing, Lanzhou 730070, China 2.School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China |
| 推荐引用方式 GB/T 7714 | Lu-Jie Zhou. Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration[J]. International Journal of Automation and Computing,2021,18(6):935-946. |
| APA | Lu-Jie Zhou.(2021).Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration.International Journal of Automation and Computing,18(6),935-946. |
| MLA | Lu-Jie Zhou."Fault Information Recognition for On-board Equipment of High-speed Railway Based on Multi-neural Network Collaboration".International Journal of Automation and Computing 18.6(2021):935-946. |
入库方式： OAI收割
来源：自动化研究所
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