Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO
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 me...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/4/404 |
| _version_ | 1798034588531949568 |
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| author | Wenlong Fu Jiawen Tan Yanhe Xu Kai Wang Tie Chen |
| author_facet | Wenlong Fu Jiawen Tan Yanhe Xu Kai Wang Tie Chen |
| author_sort | Wenlong Fu |
| collection | DOAJ |
| description | 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 <i>K</i> 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. |
| first_indexed | 2024-04-11T20:46:23Z |
| format | Article |
| id | doaj.art-7c6b3cbe11744169b89d1c4adbae5b07 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T20:46:23Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-7c6b3cbe11744169b89d1c4adbae5b072022-12-22T04:04:02ZengMDPI AGEntropy1099-43002019-04-0121440410.3390/e21040404e21040404Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSOWenlong Fu0Jiawen Tan1Yanhe Xu2Kai Wang3Tie Chen4College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, ChinaCollege of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, ChinaSchool of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaCollege of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, ChinaCollege of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, ChinaRolling 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 <i>K</i> 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.https://www.mdpi.com/1099-4300/21/4/404rolling bearingsfault diagnosisvariational mode decompositionfine-sorted dispersion entropymutation sine cosine algorithm-particle swarm optimizationsupport vector machine |
| spellingShingle | Wenlong Fu Jiawen Tan Yanhe Xu Kai Wang Tie Chen Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO Entropy rolling bearings fault diagnosis variational mode decomposition fine-sorted dispersion entropy mutation sine cosine algorithm-particle swarm optimization support vector machine |
| title | Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO |
| title_full | Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO |
| title_fullStr | Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO |
| title_full_unstemmed | Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO |
| title_short | Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO |
| title_sort | fault diagnosis for rolling bearings based on fine sorted dispersion entropy and svm optimized with mutation sca pso |
| topic | rolling bearings fault diagnosis variational mode decomposition fine-sorted dispersion entropy mutation sine cosine algorithm-particle swarm optimization support vector machine |
| url | https://www.mdpi.com/1099-4300/21/4/404 |
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