To improve the accuracy of fault diagnosis of bearing, the improved particle swarm optimization variational mode decomposition (VMD) and support vector machine (SVM) models are proposed. Aiming at the convergence effect of particle swarm optimization (PSO), dynamic inertia weight, and gradient information are introduced to improve PSO (IPSO). IPSO is used to optimize the optimal number of VMD modal components and the penalty factor, which is applied to the vibration signal decomposition. The fault sample set is constructed by calculating the multi-scale information entropy of each component signal obtained from the bearing vibration signals. At the same time, IPSO is used to optimize the support vector machine (IPSO-SVM), which is used to bearing fault diagnosis. The time-domain feature data set is used as the comparison data set, and the classical PSO, genetic algorithm, and cross-validation method are used as the comparison algorithm to verify the effectiveness of the method in this paper. The research results show that the optimized VMD can effectively decompose the vibration signal and can effectively highlight the fault characteristics. IPSO can increase the accuracy by 2% without adding additional costs. And the accuracy, volatility, and convergence error of IPSO are better than comparison algorithms.

为了提高轴承故障诊断的准确性，提出了改进的粒子群优化变分模式分解(VMD)和支持向量机(SVM)模型。针对粒子群优化（PSO）的收敛效果，引入动态惯性权重和梯度信息来改进粒子群优化（IPSO）。IPSO 用于优化 VMD 模态分量的最佳数量和惩罚因子，应用于振动信号分解。故障样本集是通过计算从轴承振动信号中得到的各分量信号的多尺度信息熵来构建的。同时利用IPSO优化支持向量机（IPSO-SVM），用于轴承故障诊断。时域特征数据集作为对比数据集，并采用经典的粒子群算法、遗传算法和交叉验证方法作为比较算法来验证本文方法的有效性。研究结果表明，优化后的VMD能够有效分解振动信号，能够有效突出故障特征。IPSO 可以在不增加额外成本的情况下将准确度提高 2%。并且IPSO的准确性、波动性和收敛误差优于对比算法。