Rolling bearing fault diagnosis based on partially ensemble empirical mode decomposition and variable predictive model-based class discrimination

An automatic fault diagnosis method for rolling bearing is proposed in this paper. Partially ensemble empirical mode decomposition (PEEMD) is developed to solve the problem of mode mixing existing in empirical mode decomposition. Compared with the ensemble empirical mode decomposition, PEEMD generates much more accurate intrinsic mode functions (IMFs) and the decomposing results are complete and orthogonal. Therefore, PEEMD is utilized to preprocess the vibration signals of rolling bearing. Moreover, the features in time, frequency domains of IMFs and ones of original data in time–frequency domain are extracted to reflect the change of fault information. To avoid the high dimension of features, Laplacian score for feature selection is utilized to sort the initial features according to their significances. The pattern recognition method, variable predictive model-based class discrimination (VPMCD) is introduced to achieve an automatic fault diagnosis. Finally, the proposed fault diagnosis method for rolling bearing is applied to analyze the experimental data and the result indicates that the proposed method can effectively diagnose the fault categories and severities of rolling bearings.