Fault Diagnosis Using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for A Railway Switch

Pattern recognition and classification have been recently applied in the area of condition monitoring to solve the problems of fault diagnosis and prognosis. Usually, the original condition monitoring data will be transformed into representative features that are of lower dimensionality, followed by the application of a classifier. However, most time-domain and frequency-domain features cannot provide good representations of non-stationary signals that are very common in machine faults. This research solves this problem by applying a Discrete Wavelet Transform (DWT) to extract the time-frequency features. Subsequently, a feed-forward artificial neural network is used as a method for fault diagnosis and classification. A railway switch system is used as a specific application in this research to validate the method using data from a field switch system. Features of the scale coefficients are then extracted using DWT for the 'Haar' wavelet type at decomposition level 9. Subsequently, the feature data are split into training, validation and testing sets, with each set containing one input feature and one corresponding desired output fault severity. After training and testing using a two-layer neural network, it is shown that in this case the switch overdriving fault with different severities could be diagnosed with 100% accuracy.