Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment
In the shaft axis monitoring of hydrogenerating unit condition monitoring and fault diagnosis, the shaft orbit is intuitive and comprehensively reflects the unit operation state, and different shaft orbits correspond to different fault types, which can accurately indicate a system vibration fault. S...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2021/6680640 |
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| _version_ | 1832552541736402944 |
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| author | Xinfeng Ge Jing Zhang Ye Zhou Jianguo Cai Hui Zhang Hongchang Hua Dong Chen Ming Zhao Jinqi Du Yuan Zheng |
| author_facet | Xinfeng Ge Jing Zhang Ye Zhou Jianguo Cai Hui Zhang Hongchang Hua Dong Chen Ming Zhao Jinqi Du Yuan Zheng |
| author_sort | Xinfeng Ge |
| collection | DOAJ |
| description | In the shaft axis monitoring of hydrogenerating unit condition monitoring and fault diagnosis, the shaft orbit is intuitive and comprehensively reflects the unit operation state, and different shaft orbits correspond to different fault types, which can accurately indicate a system vibration fault. Shaft orbit identification has important significance for vibration fault diagnosis. In getting the feature extraction and pattern recognition of a shaft orbit, the Zernike moment is better than the Hu moment; it has the advantages of a small calculation error and a high recognition rate. A rough set neural network (RS-BP hybrid model) of shaft orbit recognition is established, which uses just 13 moment eigenvalues reserved by the rough set feature selection algorithm as input variables; it has the same calculation error and recognition rate and reduces the calculation time step. The simulation of the recognition of shaft orbits shows that the hybrid model has achieved good results in the identification of shaft orbits. |
| format | Article |
| id | doaj-art-fb27fef4fdf04c708888c44894825e3e |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-fb27fef4fdf04c708888c44894825e3e2025-02-03T05:58:30ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/66806406680640Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike MomentXinfeng Ge0Jing Zhang1Ye Zhou2Jianguo Cai3Hui Zhang4Hongchang Hua5Dong Chen6Ming Zhao7Jinqi Du8Yuan Zheng9College of Energy and Electric Engineering, Hohai University, Nanjing 211100, ChinaCollege of Energy and Electric Engineering, Hohai University, Nanjing 211100, ChinaInstitute for Hydraulic Machinery, China Institute of Water Resources and Hydropower Research, Beijing 100038, ChinaChongqing Shipping Construction Development Co., Ltd., Chongqing 500000, ChinaChongqing Shipping Construction Development Co., Ltd., Chongqing 500000, ChinaCollege of Energy and Electric Engineering, Hohai University, Nanjing 211100, ChinaCollege of Energy and Electric Engineering, Hohai University, Nanjing 211100, ChinaResearch Institute of Yunnan Power Grid Co. Ltd., Kunming, ChinaYunnan Electric Test & Research Institute Group. Co. Ltd, Kunming, ChinaCollege of Energy and Electric Engineering, Hohai University, Nanjing 211100, ChinaIn the shaft axis monitoring of hydrogenerating unit condition monitoring and fault diagnosis, the shaft orbit is intuitive and comprehensively reflects the unit operation state, and different shaft orbits correspond to different fault types, which can accurately indicate a system vibration fault. Shaft orbit identification has important significance for vibration fault diagnosis. In getting the feature extraction and pattern recognition of a shaft orbit, the Zernike moment is better than the Hu moment; it has the advantages of a small calculation error and a high recognition rate. A rough set neural network (RS-BP hybrid model) of shaft orbit recognition is established, which uses just 13 moment eigenvalues reserved by the rough set feature selection algorithm as input variables; it has the same calculation error and recognition rate and reduces the calculation time step. The simulation of the recognition of shaft orbits shows that the hybrid model has achieved good results in the identification of shaft orbits.http://dx.doi.org/10.1155/2021/6680640 |
| spellingShingle | Xinfeng Ge Jing Zhang Ye Zhou Jianguo Cai Hui Zhang Hongchang Hua Dong Chen Ming Zhao Jinqi Du Yuan Zheng Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment Shock and Vibration |
| title | Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment |
| title_full | Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment |
| title_fullStr | Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment |
| title_full_unstemmed | Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment |
| title_short | Rough Set Neural Network Feature Extraction and Pattern Recognition of Shaft Orbits Based on the Zernike Moment |
| title_sort | rough set neural network feature extraction and pattern recognition of shaft orbits based on the zernike moment |
| url | http://dx.doi.org/10.1155/2021/6680640 |
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