Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform

Abstract

Wavelet transform has been widely used for the vibration signal based mechanical equipment fault diagnosis. However, the decomposition results of the discrete wavelet transform do not possess time invariant property, which may result in the loss of useful information and decrease the classification accuracy of fault diagnosis. To overcome this deficiency, a novel fault diagnosis method based on the redundant second generation wavelet packet transform is proposed. Firstly, the redundant second generation wavelet packet transform is constructed on the basis of second generation wavelet transform and redundant lifting scheme. Secondly, the vibration signals are decomposed by redundant second generation wavelet packet transform and then the faulty features are extracted from the resultant wavelet packet coefficients. Finally, the extracted fault features are given as input to classifiers for identification. The proposed method is applied for the fault diagnosis of gearbox and gasoline engine valve trains. Test results indicate that a better classification performance can be obtained by using the proposed fault diagnosis method in comparison with using second generation wavelet packet transform based method.