The vibration signals of rolling bearings are unstable and nonlinear, with weak information on failure features and extremely low signal-to-noise ratio (SNR). To solve these problems, this paper presents a failure diagnosis method based on variational mode decomposition (VMD) optimized by the improved whale optimization algorithm (IWOA) is proposed, and verifies the effectiveness of the method through a failure experiment on a test stand. Specifically, the whale optimization algorithm (WOA) was improved by replacing the linear parameter $a_1$ with a nonlinear rule. The replacement effectively improves the solution accuracy, and convergence speed. Next, the VMD parameters were optimized with IWOA. Experimental results show that the VMD optimized with IWOA can effectively and easily extract the early failure features of rolling bearings by enhancing the weak information on failure features.

滚动轴承的振动信号不稳定且是非线性的，有关故障特征的信息很少，信噪比（SNR）非常低。为了解决这些问题，本文提出了一种基于改进的鲸鱼优化算法（IWOA）优化的基于变分模分解（VMD）的故障诊断方法，并通过在试验台上进行的故障实验验证了该方法的有效性。具体来说，通过替换线性参数改进了鲸鱼优化算法（WOA）${\mathrm{一个}}_{\mathrm{1个}}$带有非线性规则。替换有效地提高了解决方案的准确性和收敛速度。接下来，使用IWOA优化了VMD参数。实验结果表明，利用IWOA优化的VMD可以通过增强关于滚动轴承失效特征的薄弱信息来有效，轻松地提取滚动轴承的早期失效特征。