关键词: 多小波相邻系数, 混合粒子群优化算法, 故障信号, 去噪

Abstract: Aiming at the problem that the traditional multi-wavelet neighbor coefficient denoising method used the uniform threshold method to obtain the threshold accuracy was not high and the signal denoising was not ideal,a multi-wavelet neighbor coefficient denoising method based on hybrid particle swarm optimization was proposed.In this method,tabu search algorithm with global optimization ability and particle swarm optimization algorithm were fused,and the fusion algorithm was introduced into the multi-wavelet neighbor coefficient denoising method to improve its way of calculating the threshold.By comparing the noise reduction results of the traditional multi-wavelet adjacent coefficient denoising method,the noise reduction algorithm based on empirical mode decomposition and the multi-wavelet denoising method based on particle swarm optimization and genetic algorithm,the results showed that the multi-wavelet adjacent coefficient denoising method based on particle swarm optimization and tabu search algorithm could not only extract more fault feature information,but also enhance the fault frequency impact characteristics and effectively suppress the background noise.

Key words: multi-wavelet neighbor coefficient, hybrid particle swarm optimization algorithm, fault signal, denoising