The Turnout Abnormality Diagnosis Based on Semi-Supervised Learning Method

Abstract

In China, the turnout abnormality very easily causes traffic accident or affects the efficiency due to the operating environment of railway transportation. The existing monitoring means are relatively backward, and mature automatic diagnosis method is lacking. In this study, a method based on semi-supervised learning algorithm for abnormal state diagnosis of turnout action curve is proposed, which is used to analyze and extract the electrical characteristics of the turnout by using the turnout action curve and the static and dynamic properties collected by the railway centralized monitoring system. The support vector machine model is used to construct the initial classifier with a small number of labeled samples, and the labeled samples are expanded from a large number of unlabeled samples. The switch curve is analyzed and diagnosed by using unlabeled data with a small amount of labeled data. The experimental results show that the method can automatically diagnose turnout electrical characteristics with high accuracy. While the cost of this method is relatively low compared with supervised learning, it can achieve higher accuracy and improve the practicability of fault diagnosis of turnout.