Improved Fault Diagnosis of Railway Switch System Using Energy-based Thresholding Wavelets (EBTW) and Neural Networks

An improved method with feature extraction and neural network classification is proposed and applied to address the fault diagnosis problem in railway switch systems. Features are extracted by analyzing the nonstationary signal time–frequency characteristics of the electromechanical machine under different fault severities. To enhance the fault diagnosis capability, an energy-based thresholding wavelets (EBTW) approach is proposed by reconstructing a low-dimensional feature vector with selected parameters. Different from the conventional discrete wavelet transform and soft-thresholding wavelet transform, the proposed method localizes and redistributes the signal energy to achieve an efficient dimension reduction. The first step of the proposed method is to perform a wavelet transform upon the original signals, whose wavelet coefficients are then thresholded and an extraction of features is made, on the basis of the energy conservation property of wavelet transforms. Once the features are extracted and selected, several state-of-the-art neural-network-based classifiers are applied for fault diagnosis by identifying the signal features from a machine normal operation and faulty operations with multiple severities. The effectiveness of the proposed method is verified using real-world operational railway switch data and compared with conventional feature extraction methods under different classifiers.