A Novel Balancing Method for Rotor Using Unsupervised Deep Learning
A novel balancing method for rotor based on unsupervised deep learning is proposed in this paper. The architecture of the proposed deep network is described. In the proposed network, compared to the supervised deep network, additional convolution layers are applied not only for the learning of the i...
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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/10.1155/2021/1800164 |
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| _version_ | 1832566258672861184 |
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| author | Shun Zhong Liqing Li Huizheng Chen Zhenyong Lu |
| author_facet | Shun Zhong Liqing Li Huizheng Chen Zhenyong Lu |
| author_sort | Shun Zhong |
| collection | DOAJ |
| description | A novel balancing method for rotor based on unsupervised deep learning is proposed in this paper. The architecture of the proposed deep network is described. In the proposed network, compared to the supervised deep network, additional convolution layers are applied not only for the learning of the inverse mapping but also for identifying the unbalanced force without labeled data. The equivalent value and position of imbalances in two correction planes are obtained. A case study of a rotor with two discs supported by sliding bearings is conducted. Preset imbalances are balanced well by the proposed method. And, using the state values at different time intervals, no extra weight trails are needed. The results show that the proposed balancing method gives consideration to both cost and accuracy. |
| format | Article |
| id | doaj-art-0d34d6b1e51c438cb6f603c3d604a1cd |
| 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-0d34d6b1e51c438cb6f603c3d604a1cd2025-02-03T01:04:31ZengWileyShock and Vibration1070-96221875-92032021-01-01202110.1155/2021/18001641800164A Novel Balancing Method for Rotor Using Unsupervised Deep LearningShun Zhong0Liqing Li1Huizheng Chen2Zhenyong Lu3Department of Mechanics and Key Laboratory of Dynamics and Control, Tianjin University, Tianjin, ChinaDepartment of Mechanics and Key Laboratory of Dynamics and Control, Tianjin University, Tianjin, ChinaInstitute of Dynamics and Control Science, Shandong Normal University, Ji’nan, ChinaInstitute of Dynamics and Control Science, Shandong Normal University, Ji’nan, ChinaA novel balancing method for rotor based on unsupervised deep learning is proposed in this paper. The architecture of the proposed deep network is described. In the proposed network, compared to the supervised deep network, additional convolution layers are applied not only for the learning of the inverse mapping but also for identifying the unbalanced force without labeled data. The equivalent value and position of imbalances in two correction planes are obtained. A case study of a rotor with two discs supported by sliding bearings is conducted. Preset imbalances are balanced well by the proposed method. And, using the state values at different time intervals, no extra weight trails are needed. The results show that the proposed balancing method gives consideration to both cost and accuracy.http://dx.doi.org/10.1155/2021/1800164 |
| spellingShingle | Shun Zhong Liqing Li Huizheng Chen Zhenyong Lu A Novel Balancing Method for Rotor Using Unsupervised Deep Learning Shock and Vibration |
| title | A Novel Balancing Method for Rotor Using Unsupervised Deep Learning |
| title_full | A Novel Balancing Method for Rotor Using Unsupervised Deep Learning |
| title_fullStr | A Novel Balancing Method for Rotor Using Unsupervised Deep Learning |
| title_full_unstemmed | A Novel Balancing Method for Rotor Using Unsupervised Deep Learning |
| title_short | A Novel Balancing Method for Rotor Using Unsupervised Deep Learning |
| title_sort | novel balancing method for rotor using unsupervised deep learning |
| url | http://dx.doi.org/10.1155/2021/1800164 |
| work_keys_str_mv | AT shunzhong anovelbalancingmethodforrotorusingunsuperviseddeeplearning AT liqingli anovelbalancingmethodforrotorusingunsuperviseddeeplearning AT huizhengchen anovelbalancingmethodforrotorusingunsuperviseddeeplearning AT zhenyonglu anovelbalancingmethodforrotorusingunsuperviseddeeplearning AT shunzhong novelbalancingmethodforrotorusingunsuperviseddeeplearning AT liqingli novelbalancingmethodforrotorusingunsuperviseddeeplearning AT huizhengchen novelbalancingmethodforrotorusingunsuperviseddeeplearning AT zhenyonglu novelbalancingmethodforrotorusingunsuperviseddeeplearning |