矿用永磁直驱电机异响声信号降噪方法研究

Research on denoising method of abnormal sound signal for direct-driven permanent magnet motor in coal mine

针对矿用永磁直驱电机异响声信号噪声干扰大,有用信号被噪声淹没难以提取的问题,提出一种融合改进VMD与小波软阈值的降噪方法。首先,利用粒子群算法优化变分模态分解算法得到分解层数k和惩罚因子α的最优参数组合,基于最优参数组合分解获得矿用永磁直驱电机异响声信号k个本征模态分量(IMF)。其次,利用加权裕度指标筛选出有效信号分量和需进一步分解的含噪分量,基于小波软阈值对含噪分量进一步降噪。最后,将有效信号分量与小波软阈值降噪后的分量重构得到最终降噪信号。应用此方法分别对仿真信号和矿用永磁直驱电机异响声信号降噪,并与其他方法对比。试验结果表明,该方法能将仿真信号信噪比提升至27.5247 dB,均方根误差降低至0.0855,实测信号信噪比提升至34.7153 dB,均方根误差降低至0.0067,降噪效果较好,为后续的故障特征提取与故障诊断工作提供数据基础。

In view of high noise interference and the difficulty of useful signal extracted for direct-driven permanent magnet motor in coal mine,a denoising method of integrating improved VMD and wavelet soft threshold is proposed.Firstly,particle swarm optimization is used to optimize the decomposition layers K and penalty factorαof the variational modal decomposition algorithm to obtain the optimal parameter combination.Based on the optimal parameter combination,K eigenmode components of abnormal sound signal for directdriven permanent magnet motor in coal mine are obtained.Secondly,the weighted margin index is used to screen out the effective signal components and the noisy components that need further decomposition.The wavelet soft threshold is used to further denoise the noisy components.Finally,the effective signal component and the wavelet soft threshold denoised component are reconstructed to obtain the final denoised signal.This method is used to denoise the simulation signal and the abnormal sound signal of direct-driven permanent magnet motor in coal mine respectively.In order to prove the validity of the method,we conduct the comparative test.The test results show that this method can increase the SNR of simulation signals to 27.5247 dB,and reduce the root mean square error to 0.0855.The SNR of measured signals is improved to 34.7153 dB,and the root mean square error is reduced to 0.0067.The method proposed can denoise effectively and provide data basis for subsequent fault feature extraction and fault diagnosis.