Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine

This paper investigates the application of the redundant second generation wavelet package transform (RSGWPT), neighborhood rough set (NRS) and support vector machine (SVM) on faulty detection, attribute reduction and pattern classification. On this basis, a novel method for mechanical faulty diagnosis based on RSGWFT, NRS and SVM is presented, which utilizes the RSGWFT to extract faulty feature parameters from the statistical characteristics of wavelet package coefficients to constitute feature vectors, and then makes the attribute reduction by NRS method to obtain the key features, lastly these key features are input into SVM to accomplish faulty pattern classification. The experimental results of the proposed method to fault diagnosis of the gearbox and gasoline engine valve trains show that this method can extract the faulty features, which have better classification ability and at the same time reduce a lot of redundant features in case of assuring the classification accuracy, accordingly improve the classifier efficiency and achieve a better classification performance. (C) 2011 Elsevier Ltd. All rights reserved.