Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing
The mine hoist sheave bearing is a large heavy-duty bearing, located in a derrick of tens of meters. Aiming at the difficulty of sheave bearing fault diagnosis, a combined fault-diagnosis method based on the improved complete ensemble EMD (ICEEMDAN) energy entropy and support vector machine (SVM) op...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/22/12/1347 |
| _version_ | 1797546323399933952 |
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| author | Ziming Kou Fen Yang Juan Wu Tengyu Li |
| author_facet | Ziming Kou Fen Yang Juan Wu Tengyu Li |
| author_sort | Ziming Kou |
| collection | DOAJ |
| description | The mine hoist sheave bearing is a large heavy-duty bearing, located in a derrick of tens of meters. Aiming at the difficulty of sheave bearing fault diagnosis, a combined fault-diagnosis method based on the improved complete ensemble EMD (ICEEMDAN) energy entropy and support vector machine (SVM) optimized by artificial fish swarm algorithm (AFSA) was proposed. Different location of the bearing defect will result in different frequency components and different amplitude energy of the frequency. According to this feature, the position of the bearing defect can be determined by calculating the ICEEMDAN energy entropy of different vibration signals. In view of the difficulty in selecting the penalty factor and radial basis kernel parameter in the SVM model, the AFSA was used to optimize them. The experimental results show that the accuracy rate of the optimized fault-diagnosis model is improved by 10% and the diagnostic accuracy rate is 97.5%. |
| first_indexed | 2024-03-10T14:28:06Z |
| format | Article |
| id | doaj.art-49f640dceabb4a74b050c24ca5c4e4d1 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T14:28:06Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-49f640dceabb4a74b050c24ca5c4e4d12023-11-20T22:47:40ZengMDPI AGEntropy1099-43002020-11-012212134710.3390/e22121347Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave BearingZiming Kou0Fen Yang1Juan Wu2Tengyu Li3College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaSchool of Mechanical Engineering, North University of China, Taiyuan 030051, ChinaCollege of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaCollege of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaThe mine hoist sheave bearing is a large heavy-duty bearing, located in a derrick of tens of meters. Aiming at the difficulty of sheave bearing fault diagnosis, a combined fault-diagnosis method based on the improved complete ensemble EMD (ICEEMDAN) energy entropy and support vector machine (SVM) optimized by artificial fish swarm algorithm (AFSA) was proposed. Different location of the bearing defect will result in different frequency components and different amplitude energy of the frequency. According to this feature, the position of the bearing defect can be determined by calculating the ICEEMDAN energy entropy of different vibration signals. In view of the difficulty in selecting the penalty factor and radial basis kernel parameter in the SVM model, the AFSA was used to optimize them. The experimental results show that the accuracy rate of the optimized fault-diagnosis model is improved by 10% and the diagnostic accuracy rate is 97.5%.https://www.mdpi.com/1099-4300/22/12/1347sheave bearingenergy entropysupport vector machineartificial fish swarm algorithm |
| spellingShingle | Ziming Kou Fen Yang Juan Wu Tengyu Li Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing Entropy sheave bearing energy entropy support vector machine artificial fish swarm algorithm |
| title | Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing |
| title_full | Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing |
| title_fullStr | Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing |
| title_full_unstemmed | Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing |
| title_short | Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing |
| title_sort | application of iceemdan energy entropy and afsa svm for fault diagnosis of hoist sheave bearing |
| topic | sheave bearing energy entropy support vector machine artificial fish swarm algorithm |
| url | https://www.mdpi.com/1099-4300/22/12/1347 |
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