Fault diagnosis of high-speed rolling bearing in the whole life cycle based on improved grey wolf optimizer-least squares support vector machines

Under the high-speed condition, the fault diagnosis of rolling bearing is difficult due to parameter limitation and local optimization. To solve these problems, a fault diagnosis method of the whole life cycle based on wavelet thresholding denoising, genetic algorithm-variational mode decomposition (GA-VMD) and improved grey wolf optimizer-least squares support vector machines (IGWO-LSSVM) is proposed. The nonlinear convergence factor is adopted to improve the global search ability of GWO, and then LSSVM is optimized by IGWO, which is used to improve the fault identification accuracy. Based on the high-speed rolling bearing test rig, this method is applied to fault diagnosis of high-speed rolling bearing in the whole life cycle. The kurtosis and approximate entropy indexes are adopted as the basis for the stage division in the degradation process. The fault diagnosis of rolling bearing in the whole life cycle is completed from the five aspects of data acquisition, stage division, data preprocessing, fault feature extraction and fault feature identification. The results show that the maximum identification accuracy of this method can reach 97%.