The application of frequency family separation method in rolling bearing fault diagnosis based on empirical mode decomposition

Rolling bearing is one of the most fragile parts in rotating machinery. Rotating machinery faults are closed related to the malfunction of bearings, which will influence the stability of the whole machine. Therefore, it is important that we detect the existence of faults and diagnose features of different faults. The method applied in this paper is based on the Empirical Mode Decomposition (EMD), the first step in Hilbert-Huang Transform, to decompose fault signal into the sum of Intrinsic Mode Functions (IMF). The IMFs that contain major bearing faults will be analyzed in frequency domain to abstract their featured fault frequencies. The result of simulation indicates that this method is effective in diagnosing normal bearing out of outer-race fault bearing and inner-race fault bearing.