OVMD-MPE群稀疏全变分去噪算法研究

轴承振动数据在采集过程中易受噪声干扰,无法有效突出微弱局部故障脉冲,从而影响轴承故障诊断效率.针对这一问题,提出了一种OVMD-MPE的群稀疏全变分去噪算法.首先,利用变分模态分解分解信号,再利用蚱蜢优化算法获得变分模态分解的最优参数;然后,计算各模态分量的经验模态分解,分离出噪声主导分量和有用分量;最后,通过群稀疏全...

Reasearch on OVMD-MPE Group Sparsity Total Variational Denoising Algorithm

Bearing vibration data is susceptible to noise interference during the acquisition process and cannot effectively highlight weak local fault pulses,thereby affecting the efficiency of bearing fault diagnosis.To solve this problem,an ovmd-mpe group sparse total variational denoising algorithm(OVMD-MPE-GSTVD)is proposed.Firstly,variational model decomposition is used to decompose the signal,and then the optimal parameters of variational model decomposition are obtained by grasshopper optimization algorithm.Then,calculate the empirical model decomposition of each modal component to separate the noise dominant component and the useful component.Finally,the dominant component of the noise is filtered by group sparse total variational denoising algorithm,and the filtered component and useful component are combined to reconstruct the denoising signal.The experimental results show that compared with the traditional denoising method,the average signal-to-noise ratio of the simulated reconstructed signal is improved by about 3.3 dB,the bearing data fault accuracy is increased to 98.9%.