A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

A novel intelligent diagnosis model based on wavelet support vector machine (WSVM) and immune genetic algorithm (IGA) for gearbox fault diagnosis is proposed. Wavelet support vector machine is a powerful novel tool for solving the diagnosis problem with small sampling, nonlinearity and high dimension. Immune genetic algorithm is developed in this study to determine the optimal parameters for WSVM with the highest accuracy and generalization ability. Moreover, the feature vectors for fault diagnosis are obtained from vibration signal that preprocessed by empirical mode decomposition (EMD). The experimental results indicate that this proposed approach is an effective method for gearbox fault diagnosis, which has more strong generalization ability and can achieve higher diagnostic accuracy than that of the artificial neural network and the SVM which has randomly extracted parameters.

► Wavelet support vector machine is used to diagnose the gearbox with multi-dimensional feature vector. ► Immune genetic algorithm is used to select appropriate free parameters for wavelet support vector machine. ► Empirical mode decomposition is applied to extract features from gearbox vibration signal. ► This proposed model has more excellent characters and better generalization performance in gearbox fault diagnosis.