Application of VPMCD method based on PLS for rolling bearing fault diagnosis

To address the non-stationary and nonlinear characteristics of vibration signals produced by rolling bearings and the noise pollution of acquired signals, a fault diagnosis method based on singular value decomposition (SVD), empirical mode decomposition (EMD) and variable predictive model-based class discrimination (VPMCD) is proposed in this paper. VPMCD is a novel pattern recognition method; however, according to the results obtained when the fault diagnosis method is applied to a small sample, the stability of the VPM constructed based on the least squares (LS) method is not sufficient, as demonstrated by the multiple correlations found between independent variables. This paper uses the partial least squares (PLS) method instead of the LS method to estimate the model parameters of VPMCD. Compared with the back-propagation neural network (BP-NN) and least squares support vector machine (LS-SVM) methods, based on numerical examples, the method presented in this paper can effectively identify a faulty rolling bearing.