A ball bearing fault diagnosis method based on wavelet and EMD energy entropy mean

According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method based on wavelet and empirical mode decomposition (EMD), energy entropy mean is put forward in this paper. Firstly, original acceleration vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs) and wavelet components, then the concept of energy entropy mean is proposed. The analysis results from energy entropy of different vibration signals show that the energy of vibration signal will change in different frequency bands when bearing fault occurs. Therefore, to diagnose ball bearing faults, we run the test rig with faulty ball bearing in various speeds and loads and collect vibration signals in each run, then calculate the energy entropy mean which indicate the fault types. The analysis results from ball bearing signals with six different faults in various working conditions show that the diagnosis approach based on using wavelet and EMD to extract the energy of different frequency bands can identify ball bearing faults accurately and effectively.