Fault Diagnosis of Ball Bearings by Wavelet Transform and Morlet Support Vector Machine and Comparison them with Empirical Mode Decomposition

In this study, a comparison among the empirical mode decomposition, ensemble empirical mode decomposition and Morlet continuous wavelet transform in fault diagnosis of bearings are performed. A Morlet wavelet support vector machine with one against one strategy that was optimized by a genetic algorithm was used for fault classification. A scale selection criterion based on the maximum relative energy to Renyi entropy ratio is proposed to determine the optimal decomposition scale for wavelet analysis. A comparison between the performances of optimized and non-optimized of support vector machines were also carried out. Vibration signals were collected by a test rig for different fault of a bearing such as normal case, bearing with inner and outer race fault, and bearing with ball fault and combine fault. After the processing of vibration signals their frequency components, several statistical features were extracted from each frequency component as input of wavelet support vector machine for the fault classification of ball bearings. For reducing of time and process of fault diagnosis, optimum feature sets of statistical parameters are selected by Utans method. K-fold cross validation method is used for evaluation of classifier. The results show that continuous wavelet transform with Morlet base has higher accuracy with respect to other methods in fault classification of bearings.