Feature extraction and selection for fault diagnosis of gear using wavelet entropy and mutual information

This paper aims to develop an complete system including signal processing, feature extraction, feature selection and classification approaches for fault diagnosis of gear by using the wavelet transform, the entropy, the mutual information and the least-square support vector machine (LS-SVM). Firstly, the vibration signals are decomposed to several wavelet coefficients. The energy of every coefficient and the singularity values (SV) of the coefficient matrix are extracted. Two type entropies means the Shannon entropy and Renyi entropy are calculated of the energy and SV distribution. Secondly, a maximum relevance and minimum redundant (mRMR) method based on the mutual information and the greedy search technique are employed to select the optimal feature subsets for gear fault classification. A cross-validation method based on the LS-SVM is proposed to determine the number of features that the optimal subset contained. Application to practical gear fault diagnosis showed that the proposed techniques provide a more effective and fast approach to gear fault diagnosis.