针对滚动轴承在复杂背景下难以实现信号降噪并进行故障诊断的问题，提出了一种基于改进的鹈鹕优化算法（IPOA）优化变分模态分解（VMD）和神经网络的故障诊断模型。首先，为了提高算法的效率，引入自适应惯性权重和非线性收敛因子对鹈鹕优化算法进行改进和优化，利用改进后的算法优化得到VMD的关键参数K和α；其次，使用得到的参数对原始信号进行VMD分解，对得到的K个IMF进行时频域的特征提取；最后，利用CNN-BiLSTM网络对滚动轴承的不同状态进行识别。滚动轴承数据集试验表明，采用IPOAVMD方法提取的特征数据，相较于使用POA-VMD，PSO-VMD，VMD方法提取的特征数据，经过CNN-BiLSTM网络进行故障诊断时，在大基数的条件下，准确率仍有所提升，在测试集数据集上的平均准确率达到了99.867%。

In order to address the challenge of signal denoising and fault diagnosis for rolling bearings in complex environments, a fault diagnosis model based on an improved pelican optimization algorithm (IPOA) combined with variational mode decomposition (VMD) and neural networks was proposed. To enhance the algorithm efficiency, adaptive inertia weights and nonlinear convergence factors were first introduced to improve and optimize the pelican optimization algorithm. The improved algorithm was then used to optimize and determine the critical parameters K and α of VMD. Subsequently, the optimized parameters were applied for VMD decomposition of the original signal, and time-frequency domain features were extracted from the resulting K intrinsic mode functions (IMFs). Finally, a CNN-BiLSTM network was employed to classify the different states of rolling bearings. Experimental results on a rolling bearing dataset demonstrate that the feature data extracted using the IPOA-VMD method achieves higher fault diagnosis accuracy compared to feature data extracted using POA-VMD, PSO-VMD and VMD methods. On the test dataset, the proposed method achieves an average diagnostic accuracy of 99.867% with a large sample size.