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

doi:10.1007/s11633-021-1291-2

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	Fault Classification for On-board Equipment of High-speed Railway Based on Attention Capsule Network
	Lu-Jie Zhou 1
刊名	International Journal of Automation and Computing
	2021
卷号	18 期号:5 页码:814-825
关键词	On-board equipment fault classification capsule network attention mechanism focal loss
ISSN号	1476-8186
DOI	10.1007/s11633-021-1291-2
英文摘要	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.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45465]
专题	自动化研究所_学术期刊_International Journal of Automation and Computing
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. 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.(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."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.