针对变分模态分解（variational mode decomposition, VMD）人为选择惩罚因子和模态个数难以有效分解振动信号，导致故障诊断识别率低的问题，提出一种基于改进VMD与熵值特征融合的滚动轴承故障特征提取方法。首先，利用Cubic混沌映射、黄金正弦策略改进海象优化算法（improved walrus optimization algorithm, IWaOA）。然后用IWaOA优化VMD，找出最佳的惩罚因子和模态个数，再使用VMD对振动信号进行分解并计算各个模态分量的7种熵值特征，建立IWaOA-VMD特征提取模型。其次，采用线性判别分析（linear discriminant analysis, LDA）方法对7种熵值特征进行降维融合，得到融合后的特征向量输入反向传播（back propagation, BP）神经网络中进行识别，建立LDA-BP故障识别模型。最后，对本文方法进行实验验证。结果表明：所提方法在对凯斯西储大学轴承数据集上的故障识别准确率达99.58%，且在强噪声干扰下达到92%以上的准确率；为验证其适用性，进一步在对西安交通大学XJTU-SY数据集上的故障识别准确率达到100%，证实了所提方法的噪声鲁棒性与多源数据适用性。

A rolling bearing fault feature extraction method based on improved VMD and entropy feature fusion was proposed to address the problem of low fault diagnosis recognition rate caused by the difficulty in effectively decomposing vibration signals due to the manually selected penalty factor and the number of modes in variational mode decomposition (VMD). Firstly, the improved walrus optimization algorithm (IWaOA) was obtained by using cubic chaotic mapping and the golden sine strategy. Then, IWaOA was used to optimize VMD and find the optimal penalty factor and the number of modes. Then, VMD was used to decompose the vibration signal and calculate the seven entropy value features for each mode component, and establish an IWaOA-VMD feature extraction model. Secondly, the linear discriminant analysis (LDA) method was used to reduce the dimensional fusion of the seven entropy value features, and the fused feature vectors were input into the back propagation (BP) neural network for identification, so as to established the LDA-BP fault identification model. Finally, the proposed method was verified by experiments. The results show that the fault recognition accuracy of the proposed method on the Case Western Reserve University bearing data set is 99.58%, and the accuracy under strong noise interference is more than 92%. In order to verify its applicability, the accuracy of fault identification on the XJTU-SY data set of Xi'an Jiaotong University reaches 100%, which verifies the noise robustness and multi-source data applicability of the proposed method.