SVMWPE-HLLE Based Fault Diagnosis Approach for Rolling Bearing

Multi-scale permutation entropy (MPE) is an analytical method describing the complexity of time series, which has been applied to the fault diagnosis of rolling bearings. To solve the problems of MPE in coarse-grained process and permutation entropy calculation, multi-scale weighted permutation entropy based on sliding variance (SVMWPE) was proposed in this paper. By analyzing WGN signal and 1/f noise signal, the parameter selections for SVMWPE were studied, and the stability and superiority were investigated by comparing SVMWPE with MPE, MWPE, and SVMPE. The high-dimensional matrix obtained by MPE feature extraction to pattern recognition was solved by introducing Hessian local linear embedding (HLLE) dimension reduction method, and the feature extraction method based on SVMWPE-HLLE was proposed. The clustering effect was studied by comparing SVMWPE-HLLE with SVMWPE-LLE through the analysis of three simulation signals. Fault diagnosis method for rolling bearing was proposed by combining SVMWPE-HLLE with extreme learning machine (ELM), which was applied to two experimental cases of rolling bearings for analysis. The experimental results showed that the proposed method can realize intelligent diagnosis of different fault types and degrees of rolling bearing, and the fault recognition rate of the proposed method was higher than other methods.