Switch machine,as the key equipment for railway operation scheduling,is prone to malfunction in the work-ing process because of its harsh external environment and complex internal equipment structure.Aiming at the electro-hy-draulic switch machines widely used in high-speed railways,this paper proposed a fault intelligent diagnosis method.The idea of contrastive learning was used to regularize the feature space based on the oil pressure detection signals of the left and right hydraulic cylinders of the switch machine.An instance-level weighting strategy was used to enhance model gen-eralization.A variety of data enhancement methods were used to improve model robustness.Finally,through the operation fault experiment of the electro-hydraulic switch machine,the effectiveness and superiority of the electro-hy-draulic switch machine fault intelligent diagnosis based on contrastive learning were verified.
维普
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