基于LMD近似熵和改进PSO-ELM的轴承故障诊断

Bearing Fault Diagnosis Based on LMD Approximate Entropy and Improved PSO-ELM

针对滚动轴承故障特征提取与故障识别困难的问题,提出局部均值分解(LMD)近似熵和改进粒子群优化的极限学习机(PSO-ELM)结合的滚动轴承故障诊断方法。将不同工况信号用LMD分解为一系列乘积分量,不同工况的信号在不同频带的近似熵值会发生改变,结合相关性系数选出前3个分量,计算近似熵定值作为输入的特征向量。针对PSO早熟收敛的缺点,引入自适应权重法与DE算法对PSO进行改进,将特征值输入到改进PSO-ELM网络模型中,对滚动轴承不同工况进行故障识别与分类。结果表明,基于LMD近似熵和改进粒子群优化的ELM不仅能够识别滚动轴承的故障类型,并且有更高的分类正确率,验证了该方法的可行性。

Aiming at the difficulty of fault feature extraction and fault identification of rolling bearing,a fault diagnosis method for rolling bearings based on local mean decomposition(LMD)approximate entropy and improved particle swarm optimization based extreme learning machine(PSO-ELM)was proposed.The signals under different working conditions were decomposed into a series of multiplicative components by LMD.The approximate entropy values of the signal under different working conditions changed in different frequency bands.Combined with the correlation coefficient,the first three components were selected,and the approximate entropy value was calculated as the input eigenvector.Aiming at the shortcomings of premature convergence of PSO,the adaptive weight method and DE algorithm were introduced to improve the PSO,and the eigenvalues were input into the improved PSO-ELM network model,the fault identification and classification was performed for different working conditions of rolling bearings.The results show that the ELM based on LMD approximate entropy and improved particle swarm optimization can not only identify the fault types of rolling bearings,but also have a higher classification accuracy,which verifies the feasibility of the method.