A novel roller bearing fault diagnosis method based on the wavelet extreme learning machine

The safety and reliability of roller bearing always have significant importance in rotating machinery. It is needful to build an efficient and excellent accuracy method to monitoring and diagnosis the baring failure. A novel method is presented in this paper to classify the fault feature by wavelet function and extreme learning machine(ELM) that take into account the high accuracy and efficient. The morlet wavelet function was constructed as the activation function of ELM neural nodes. In order to construct the best wavelet basis function. The minimum Shannon entropy and SVD methods are used to select the optimal shape factor and scale parameter for the morlet wavelet, respectively. The proposed method is applied to practical classification and fault diagnosis of roller bearing. The result show that the proposed method is more reliable and suitable than conventional neural networks and other ELM methods for the defect diagnosis of roller bearing.