Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy mean and root mean square (RMS)

According to the non-stationary characteristics of ball bearing fault vibration signals, a ball bearing fault diagnosis method using FFT and wavelet energy entropy mean and root mean square (RMS), energy entropy mean is put forward. in this paper, Firstly, original rushing vibration signals is transformed into a frequency domain, and is comminuted wavelet components, then the theory of energy entropy mean and root mean square is proposed. The analysis results from energy entropy and root mean square of different vibration signals show that the energy and root mean square 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 and root mean square 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 FFT to extract the energy and root mean square of different frequency bands can identify ball bearing faults accurately and effectively. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis.