Knowledge Commons of Institute of Automation,CAS
Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network | |
Lu-Jie Zhou1; Jian-Wu Dang1,2; Zhen-Hai Zhan1 | |
发表期刊 | International Journal of Automation and Computing
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ISSN | 1476-8186 |
2021 | |
卷号 | 18期号:5页码:814-825 |
摘要 | 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. |
关键词 | On-board equipment fault classification capsule network attention mechanism focal loss |
DOI | 10.1007/s11633-021-1291-2 |
七大方向——子方向分类 | 其他 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45465 |
专题 | 学术期刊_Machine Intelligence Research |
作者单位 | 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 |
推荐引用方式 GB/T 7714 | Lu-Jie Zhou,Jian-Wu Dang,Zhen-Hai Zhan. Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network[J]. International Journal of Automation and Computing,2021,18(5):814-825. |
APA | Lu-Jie Zhou,Jian-Wu Dang,&Zhen-Hai Zhan.(2021).Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network.International Journal of Automation and Computing,18(5),814-825. |
MLA | Lu-Jie Zhou,et al."Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network".International Journal of Automation and Computing 18.5(2021):814-825. |
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IJAC-2020-07-169.pdf(1208KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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