A Novel Rolling Bearing Fault Detection Method based on Wavelet Transform and Empirical Mode Decomposition

It is very difficult to diagnose the fault information of high-speed rolling bearings for its complex working environment. A fault diagnosis method based on wavelet transform and empirical mode decomposition is proposed. Firstly, the method uses wavelet decomposition and reconstruction algorithm to denoise the vibration signal. Then, the improved empirical mode decomposition method based on rational cubic Hermite spline is used to decompose the data, and intrinsic mode functions are obtained. Then, the spectrum analysis is carried out to diagnose the fault state. Lastly this validity of the method is verified by the actual sampling data.