An intelligent fault identification method of rolling bearings based on SVM optimized by improved GWO

Abstract

To improve the accuracy and the recognition efficiency of a bearing fault diagnosis, a fault diagnosis method based upon the improved grey wolf optimization (IGWO) algorithm and support vector machine (SVM) is proposed in the following manners. First, the data are pre-processed by using the set ensemble empirical mode decomposition (EEMD), Shannon wavelet packet entropy (SWPE), and principal component analysis (PCA). Next, the idea of updating the host bird nest by the cuckoo search (CS) optimization algorithm is introduced into the grey wolf optimization (GWO) algorithm to obtain the IGWO algorithm. Then, the SVM is optimized by the IGWO algorithm to obtain optimal parameters for a new diagnostic model. This model improves the problem where the algorithm easily to falls into a local optimum. The learning ability and the generalization ability of the SVM are also enhanced. Finally, the effectiveness of the optimization model is tested by two different bearing data sets. The results show that compared to the genetic algorithm (GA), particle swarm optimization (PSO) and GWO algorithm optimization, the IGWO algorithm can be more accurate and efficient when diagnosing bearings.