Rolling bearings act as key parts in many items of mechanical equipment and any abnormality will affect the normal operation of the entire apparatus. To diagnose the faults of rolling bearings effectively, a novel fault identification method is proposed by merging variational mode decomposition (VMD), average refined composite multiscale dispersion entropy (ARCMDE) and support vector machine (SVM) optimized by multistrategy enhanced swarm optimization in this paper. Firstly, the vibration signals are decomposed into different series of intrinsic mode functions (IMFs) based on VMD with the center frequency observation method. Subsequently, the proposed ARCMDE, fusing the superiorities of DE and average refined composite multiscale procedure, is employed to enhance the ability of the multiscale fault-feature extraction from the IMFs. Afterwards, grey wolf optimization (GWO), enhanced by multistrategy including levy flight, cosine factor and polynomial mutation strategies (LCPGWO), is proposed to optimize the penalty factor C and kernel parameter g of SVM. Then, the optimized SVM model is trained to identify the fault type of samples based on features extracted by ARCMDE. Finally, the application experiment and contrastive analysis verify the effectiveness of the proposed VMD-ARCMDE-LCPGWO-SVM method.

中文翻译：

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融合平均精细复合多尺度色散熵辅助特征提取和支持向量机的滚动轴承智能故障识别与多策略增强群算法

滚动轴承是许多机械设备中的关键部件，任何异常都会影响整个设备的正常运行。为了有效地诊断滚动轴承的故障，提出了一种新的故障识别方法：融合变分模式分解（VMD），平均精细复合多尺度色散熵（ARCMDE）和支持向量机（SVM），并通过多策略增强群优化算法进行了优化。 。首先，利用中心频率观测方法，基于VMD将振动信号分解为不同的本征函数（IMF）序列。随后，提出的ARCMDE融合了DE的优势和平均精细复合多尺度程序，被用来增强从IMF提取多尺度断层特征的能力。然后，C和SVM的内核参数g。然后，基于ARCMDE提取的特征，训练优化的SVM模型以识别样本的故障类型。最后，通过应用实验和对比分析验证了所提出的VMD-ARCMDE-LCPGWO-SVM方法的有效性。