A Novel Fault Diagnosis Method for High-Speed Railway Turnout Based On DCAE-Logistic Regression

Abstract

Turnout fault diagnosis is an essential means to reduce maintenance cost and ensure safety in high-speed railway operation. Aiming to improve diagnosis accuracy, this paper proposes a novel hybrid deep learning framework combining Deep Convolutional Auto-encoder (DCAE) and Logistic Regression (LR) for turnout fault diagnosis. The raw turnout current signal data is converted to 2-D image, and DCAE is employed to automatically extract features of 2-D images. Then the feature data is fed into LR for turnout fault diagnosis. Thanks to extracting features automatically, the proposed method can overcome the weakness that manual feature extraction depends on much expertise and prior knowledge in traditional data-driven diagnosis method. The proposed method can achieve an accuracy of 99.52% on historical field data collected from a real high-speed railway turnout.