A Parameter-Adaptive VME Method Based on Particle Swarm Optimization for Bearing Fault Diagnosis

In the decomposition process of variational mode extraction (VME), it is hard to choose the approximate center frequency and the weighting coefficient reasonably. To address this issue, this paper aims to present a new bearing fault diagnosis scheme integrating VME with particle swarm optimization (PSO). Firstly, a new index combining correlation coefficient, L-kurtosis and information entropy is constructed. Then, the PSO is employed to optimize the inside VME parameters by combining the new index as the fitness function. Finally, the desired mode is analyzed by envelope demodulation to identify the fault characteristics. The effectiveness of the approach is validated using the experimental data sets collected from bearings with damaged outer race. A comparison of the PSO-VME method using the weighted kurtosis (KCI) as the fitness function highlights the superiority of the new index. Furthermore, comparisons with the PSO-VMD method and fast kurtogram method further validate the efficiency and accuracy of the proposed method.