【摘要】 针对滚动轴承故障信号具有非线性、非平稳、噪声强的特点，提出了一种基于参数自寻优变分模态分解(variational modal decomposition, VMD)的信号降噪方法。以模态复合熵作为适应度函数，采用改进粒子群算法进行VMD参数自适应寻优，确定变分模态分解最优模态数K和二次惩罚因子α;基于最优K和α,对原始信号进行VMD分解，得到K个本征模态函数(intrinsic mode function, IMF)分量；利用相关系数筛选法，进行模态分量的有效模态和含噪模态识别，利用小波阈值去噪方法对含噪模态进行去噪处理；将有效模态与去噪后的模态进行重构，实现信号降噪。分别用滚动轴承故障仿真信号和试验信号进行验证，并与EMD降噪方法进行比较，结果表明该方法可有效提高故障信号的信噪比，降噪效果明显，有利于滚动轴承故障特征的提取。更多还原

【Abstract】 Here, aiming at rolling bearing fault signals having characteristics of nonlinear, non-stationary and strong noise, a signal denoising method based on parametric self-optimizing of variational mode decomposition(VMD) was proposed. Taking the mode compound entropy as the fitness function, VMD parameters were adaptively optimized by using the improved particle swarm optimization(IPSO) algorithm to determine VMD’s optimal mode number K and the quadratic penalty factor α. Based on the optimal K and α, VMD was performed for the original signal to obtain K intrinsic mode functions(IMFs). The correlation coefficient screening method was used to identify effective modes and noisy modes, and the wavelet threshold denoising method was used to denoise noisy modes. Effective modes and the denoised modes were used to reconstruct the denoised signal to realize signal denoising. Simulated signals and test signals of rolling bearing faults were used to verify the proposed method, the results were compared with those obtained using EMD denoising method. The results showed that the proposed method can effectively improve signal-to-noise ratios of faulty signals; its denoising effect is obvious to be conducive to extracting rolling bearing fault features.更多还原