铁道科学与工程学报2025，Vol.22Issue(1)：404-415,12.DOI:10.19713/j.cnki.43-1423/u.T20240574
基于无标签自监督表示学习的转辙机故障诊断方法研究
An unlabeled self-supervised fault diagnosis method for switch machines based on contrastive learning of representations
摘要
Abstract
Switch machines are key signaling devices in railway turnout systems,essential for the control of track switching,where accurate diagnosis of their operational status is crucial for the safety and reliability of train operation.This study proposed a novel self-supervised fault diagnosis method of switch machines based on contrastive learning of representations,addressing the challenges associated with the lack of labeled data and the rarity of fault samples in field data.This approach initially transforms the action curves of switch machines into an image format,allowing better extraction of latent features using an autoencoder.A representation learning model is then devised,leveraging the inherent similarities among data of switch machines with the same fault type.This model supervises the training process by comparing similarities across batches of data,thereby clustering representations with high similarity and uncovering the latent structural classification of the data.Furthermore,the generalization advantage of contrastive learning allows for using data from various types of switch machines to enhance the training effect.Ultimately,a downstream classification network is trained to classify these features,facilitating a fault diagnosis model independent of manually labeled data.Experimental findings suggest that,compared to traditional autoencoders,the contrastive representation learning model more effectively differentiates between different fault types in switch machine monitoring data.When trained on unlabeled field data from ZDJ9-type switch machines,the model achieves a fault diagnosis accuracy of 99.63%on the test dataset;this accuracy increases to 99.88%when data from both ZDJ9-type and ZYJ7-type switch machines are combined for training.This represents an eight percentage point improvement over traditional unsupervised learning models and is comparable to the performance of supervised models.By merging the benefits of unsupervised models not relying on manual labels and the high fault diagnosis accuracy of supervised models,this method presents a new feasible solution for fault diagnosis of switch machines in the railway field.关键词
转辙机/故障诊断/自监督/对比学习/表示学习Key words
switch machine/fault diagnosis/self-supervised/contrastive learning/representations learning分类
交通工程引用本文复制引用
郑启明,王小敏,江磊..基于无标签自监督表示学习的转辙机故障诊断方法研究[J].铁道科学与工程学报,2025,22(1):404-415,12.基金项目
中国国家铁路集团有限公司科技研发计划(P2021G053,N2022G010) （P2021G053,N2022G010）
敏捷智能计算四川省重点实验室开放式基金资助项目 （）
