Bearing fault diagnosis method based on variational mode decomposition optimized by CS-PSO

Abstract

In order to solve the problem that bearing vibration signal fault feature is difficult to extract effectively under noise, a fault diagnosis method based on Variational Mode Decomposition (VMD) optimized by Cuckoo Search (CS) and Particle Swarm Optimization (PSO) is proposed. The effect of VMD is affected by the number of modes and the penalty parameter. The Levy flight strategy and elimination mechanism of the CS algorithm is added to PSO algorithm and the position updating process is optimized. According to the correlation coefficient and envelope entropy of the Intrinsic Mode Function (IMF), the objective function is constructed to search for the optimal combination of VMD mode number and penalty parameter. The bearing fault type is determined by analyzing the IMF envelope spectrum of the optimal objective function. The fault diagnosis classification task upon the bearing sample data from Case Western Reserve University demonstrates that the proposed CS-PSO algorithm improves the model’s classification performance by 5%, which is on average 7.3% higher than other state-of-the-art models. This model can provide an important reference for the accuracy of bearing fault diagnosis.