基于PSO优化VMD算法的轴承振动信号重构及故障诊断

Vibration Signal Reconstruction and Fault Diagnosis of Rolling Bearings Based on PSO Optimization VMD Algorithm

为了进一步提高轴承故障诊断效率,利用多重分形法描述信号特征,设计了一种通过粒子群优化算法(PSO)优化变分模态分解(VMD)分解有限带宽本征模态分量(BIMF)方法,达到重构阈值信号的目的。研究结果表明:滚珠故障产生了最复杂的振动状态,外圈故障和正常信号形成了相近的振动特性。以PSO优化VMD方法分解与重构的轴承在正常状态下达到了1.4m/s的最大速度幅值,在滚珠、内圈与外圈故障下时形成了比正常状态更高的最大速度幅值,依次达到9.32m/s、4.61m/s与2.35m/s。经过重构的振动信号表现出了很强的故障分类性能,只产生了一组错误的分类结果,可以判断根据峭度与多重分形谱因子对改进变分模态分解预处理后的轴承状态实施故障诊断能够满足可行性与有效性要求。

In order to further improve the efficiency of bearing fault diagnosis,the multifractal method is used to describe the signal characteristics,and a particle swarm optimization(PSO)algorithm is used to optimize the variational mode decomposition(VMD)to decompose the band-limited intrinsic mode function(BIMF),so as to achieve the purpose of threshold signal reconstruction.The results show that the ball fault produces the most complex vibration state,and the outer ring fault and the normal signal display similar vibrational characteristics.The bearing decomposed and reconstructed by the PSO optimized VMD method reaches the maximum speed of 1.4 m/s under the normal condition,and the maximum speed of 9.32 m/s,4.61m/s and 2.35 m/s under ball,inner and outer ring faults is higher than that under the normal condition.The reconstructed vibration signals show strong fault classification performance,and only one group of wrong classification results are generated.It can be judged that the fault diagnosis of the rolling bearing state after preprocessing with improved variational mode decomposition can meet the feasibility and effectiveness requirements according to the kurtosis and multifractal spectrum factor.