针对强噪声背景下轴承故障特征提取困难的问题，提出一种基于奇异值分解和参数优化变分模态分解联合降噪的轴承故障特征提取方法(SSVMD)：首先，对原始信号进行奇异值分解(Singular Value Decomposition，SVD)处理，运用奇异值差分谱法选取有效奇异值并将原始信号重构得到初步降噪信号；其次，为防止故障信息丢失，将残余信号进行麻雀算法(Sparrow Search Algorithm，SSA)优化的变分模态分解(Variational Mode Decomposition，VMD）算法处理，得到最佳的模态个数K和惩罚参数α，选取峭度值最大、包络熵最小的IMF分量与初步降噪信号叠加得到最终降噪信号，并对信号进行包络分析；最后，通过仿真和试验数据分析得出，该方法能在信噪比很低的情况下降低噪声含量并提取轴承故障特征，为设备的状态监测和故障诊断提供理论依据。

Aiming at the difficulty of bearing fault feature extraction under strong noise background, a combined denoised bearing fault feature extraction method based on singular value decomposition and variational mode decomposition of parameter optimization (SSVMD) was proposed in this paper.Firstly, the original signal was processed with singular value decomposition (SVD). The method used the singular value difference spectrum to select effective singular values, and reconstructed the original signal to obtain the preliminary denoised signal. Secondly, to prevent the loss of fault information, the residual signal was processed by the variational mode decomposition (VMD) optimized by sparrow search algorithm (SSA), the optimal number of modes K and penalty parameters α were obtained. The IMF component with the maximum kurtosis value and the minimum envelope entropy was selected to overlay with the preliminary denoised signal to obtain the final denoised signal, and the envelope analysis of the signal was carried out. Finally, the simulation and experimental data analyses were carried out. The results show that this method can effectively reduce the noise content and extract the bearing fault feature under the condition of a low signaltonoise ratio, to provide a theoretical basis for equipment condition monitoring and fault diagnosis.

TH133.3

国家科技重大专项（J2019-IV-0021，J2019-I-0012）；湖北省自然科学基金（2020CFB536）