Fault diagnosis of rolling element bearings based on Multiscale Dynamic Time Warping

A fault recognition method for rolling element bearings based on Multiscale Dynamic Time Warping (MDTW) is proposed in this paper. After preprocessing using Empirical Mode Decomposition (EMD), CWs are extracted from vibration signals. The normalization of CWs is necessary to eliminate the influence of amplitude variations before using MDTW. Following this process, one CW of the normal condition is selected as a template to calculate the distance of DTW (DDTW) with other CWs. The calculated DDTW results can then be utilized to classify the bearing conditions since different conditions have different DDTW bands. The proposed method is validated by the data from Bearing Data Centre of Case Western Reserve University. The analysis results indicate that the influence of variable speed and different defect sizes can be effectively eliminated by DTW.