Application of morphological component analysis for rolling element bearing fault diagnosis

Periodical impulses in vibration signals are key features in rolling element bearing fault diagnosis. Based on an overcomplete dictionary composed of different morphological atoms, morphological component analysis can be used to extract the signal components of different types of morphologies. A new morphological component analysis method based on a novel over-completed dictionary was proposed herein. According to morphological differences between components in rolling element bearing fault vibration signal, the method after improved dictionary could more targeted to extract impulse components containing fault feature. Then through envelope spectrum analysis, the fault characteristic frequency was extracted accurately, and rolling element bearing local faults were diagnosed. Compared with the Fast Kurtogram method for bearing fault diagnosis, the new method could avoid non-accuracy and non-optimality problems caused by artificial choice of resonance band, and improve the effectiveness of fault diagnosis. By analyzing both the simulation signal and the experimental dataset of rolling element bearing faults, the proposed method is validated.