Fault Diagnosis for Rolling Bearings Based on Composite Multiscale Fine-Sorted Dispersion Entropy and SVM With Hybrid Mutation SCA-HHO Algorithm Optimization

被引:101
作者
Fu, Wenlong [1 ,2 ]
Shao, Kaixuan [1 ,2 ]
Tan, Jiawen [1 ,2 ]
Wang, Kai [1 ,2 ]
机构
[1] China Three Gorges Univ, Coll Elect Engn & New Energy, Yichang 443002, Peoples R China
[2] China Three Gorges Univ, Hubei Prov Key Lab Operat & Control Cascaded Hydr, Yichang 443002, Peoples R China
基金
中国国家自然科学基金;
关键词
Fault diagnosis; variational mode decomposition; composite multiscale fine-sorted dispersion entropy; support vector machine; hybrid mutation SCA-HHO; EMPIRICAL MODE DECOMPOSITION; PARTICLE SWARM; STRATEGY; NETWORK; SYSTEM;
D O I
10.1109/ACCESS.2020.2966582
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The health condition of rolling bearing possesses a significant impact on the safety and efficiency of rotating machinery. Accordingly, to diagnose the faults in rolling bearings effectively and accurately, a novel hybrid approach coupling variational mode decomposition (VMD), composite multiscale fine-sorted dispersion entropy (CMFSDE) and support vector machine (SVM) optimized by mutation sine cosine algorithm and Harris hawks optimization (MSCAHHO) is proposed in the paper. Firstly, VMD is employed to decompose raw vibration signals with various fault types into different sets of intrinsic mode functions (IMFs) to weaken the non-stationarity of signals, before which the parameter of VMD is decided through central frequency observation method. Subsequently, CMFSDE is put forward in this paper to analyze the complexity of fault signals by fully considering the relationship between neighboring elements based on composite multiscale technique, with which the representative features of different fault samples are extracted to construct feature vectors. Later, an enhanced hybrid optimization approach called MSCAHHO is proposed by integrating sine cosine algorithm (SCA) and a periodic mutation strategy to improve Harris hawks optimization (HHO). Then, MSCAHHO is employed to optimize the parameters of SVM, after which the optimal SVM model is utilized for fault classification. Finally, the performance of the proposed methodology is evaluated with four validity indices through comparative experiments. The experimental results reveal that the proposed VMD-CMFSDE-MSCAHHO-SVM method achieves favorable diagnosis results comparing with other relevant methods.
引用
收藏
页码:13086 / 13104
页数:19
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