期刊
ENTROPY
卷 21, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/e21040404
关键词
rolling bearings; fault diagnosis; variational mode decomposition; fine-sorted dispersion entropy; mutation sine cosine algorithm-particle swarm optimization; support vector machine
资金
- National Natural Science Foundation of China (NSFC) [51741907, 51809099]
- Hubei Provincial Major Project for Technical Innovation [2017AAA132]
- Open Fund of Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station [2017KJX06]
- Research Fund for Excellent Dissertation of China Three Gorges University [2019SSPY070]
Rolling bearings are a vital and widely used component in modern industry, relating to the production efficiency and remaining life of a device. An effective and robust fault diagnosis method for rolling bearings can reduce the downtime caused by unexpected failures. Thus, a novel fault diagnosis method for rolling bearings by fine-sorted dispersion entropy and mutation sine cosine algorithm and particle swarm optimization (SCA-PSO) optimized support vector machine (SVM) is presented to diagnose a fault of various sizes, locations and motor loads. Vibration signals collected from different types of faults are firstly decomposed by variational mode decomposition (VMD) into sets of intrinsic mode functions (IMFs), where the decomposing mode number K is determined by the central frequency observation method, thus, to weaken the non-stationarity of original signals. Later, the improved fine-sorted dispersion entropy (FSDE) is proposed to enhance the perception for relationship information between neighboring elements and then employed to construct the feature vectors of different fault samples. Afterward, a hybrid optimization strategy combining advantages of mutation operator, sine cosine algorithm and particle swarm optimization (MSCAPSO) is proposed to optimize the SVM model. The optimal SVM model is subsequently applied to realize the pattern recognition for different fault samples. The superiority of the proposed method is assessed through multiple contrastive experiments. Result analysis indicates that the proposed method achieves better precision and stability over some relevant methods, whereupon it is promising in the field of fault diagnosis for rolling bearings.
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