Research about rolling element bearing fault diagnosis based on mathematical morphology and sample entropy

In view of the non-linear and non-stationary of the rolling element bearing fault signal, the method of mathematical morphology analysis is introduced into the rolling element bearing fault diagnosis. Multi-scale morphological transform is applied to the analysis of the bearing signals. To describe the complexity of pattern spectrum curves by using sample entropy, and its value as the input vector of the neural network is used to realize the fault pattern classification by using the back-propagation (BP) neural network. Experimental results show that this method is effective.