Shi Huibin, Fu Wenlong, Li Bailin, Shao Kaixuan, Yang Duanhao
College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China.
Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China.
Entropy (Basel). 2021 Apr 25;23(5):527. doi: 10.3390/e23050527.
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 and kernel parameter 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.
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故障，本文提出了一种将变分模态分解（VMD）、平均精细复合多尺度分散熵（ARCMDE）和通过多策略增强群优化算法优化的支持向量机（SVM）相结合的新型故障识别方法。首先，基于VMD和中心频率观测方法，将振动信号分解为不同的本征模态函数（IMF）序列。随后，所提出的融合了分散熵（DE）和平均精细复合多尺度过程优势的ARCMDE，被用于增强从IMF中提取多尺度故障特征的能力。之后，提出了一种通过包括莱维飞行、余弦因子和多项式变异策略（LCPGWO）的多策略增强的灰狼优化算法（GWO），来优化SVM的惩罚因子和核参数。然后，基于ARCMDE提取的特征，训练优化后的SVM模型来识别样本的故障类型。最后，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。
