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Entropy 2019, 21(4), 404; https://doi.org/10.3390/e21040404

Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO

1,2,*, 1,2, 3, 1,2 and 1,2
1
College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
2
Hubei Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 443002, China
3
School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Received: 22 March 2019 / Revised: 13 April 2019 / Accepted: 14 April 2019 / Published: 16 April 2019
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Abstract

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. View Full-Text
Keywords: rolling bearings; fault diagnosis; variational mode decomposition; fine-sorted dispersion entropy; mutation sine cosine algorithm-particle swarm optimization; support vector machine rolling bearings; fault diagnosis; variational mode decomposition; fine-sorted dispersion entropy; mutation sine cosine algorithm-particle swarm optimization; support vector machine
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Fu, W.; Tan, J.; Xu, Y.; Wang, K.; Chen, T. Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO. Entropy 2019, 21, 404.

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