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A turnout switch machine is key equipment in a railway, and its fault condition has an enormous impact on the safety of train operation. Electrohydraulic switch machines are increasingly used in high-speed railways, and how to extract effective fault features from their working condition monitoring signal is a difficult problem. This paper focuses on the sectionalized feature extraction method of the oil pressure signal of the electrohydraulic switch machine and realizes the fault detection of the switch machine based on this method. First, the oil pressure signal is divided into three stages according to the working principle and action process of the switch machine, and multiple features of each stage are extracted. Then the max-relevance and min-redundancy (mRMR) algorithm is applied to select the effective features. Finally, the mini batch k-means method is used to achieve unsupervised fault diagnosis. Through experimental verification, this method can not only derive the best sectionalization mode and feature types of the oil pressure signal, but also achieve the fault diagnosis and the prediction of the status of the electrohydraulic switch machine.

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The railway system is an important part of the transportation system. Its scheduling process is carried out by the switch machines. The accuracy of determining the health status of the switch machines is related to the operational efficiency and reliability of the whole system. However, manual fault diagnosis for these machines is always unstable and expensive. The intelligent fault diagnosis (IFD) method can perform accurate fault diagnosis at low cost and high efficiency, but requires a large amount of labeled data. In this case, this study realizes the Feature Pseudo-Fusion (FPF) of left and right oil pressure signals of the electro-hydraulic switch machine. It uses contrastive learning to regularize the feature representation of original signals. Based on FPF, a fault diagnosis method applicable to electro- hydraulic switch machines is constructed. This method reduces the need for labeled data laterally without introducing additional measurement content to the field signal acquisition system. The effectiveness of FPF and the superiority of the fault diagnosis method have been verified through experiments.

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