A recurrent neural network‐based rotor displacement estimation method for eight‐pole active magnetic bearing
Abstract Active magnetic bearing (AMB) is a key technology in high‐speed rotating machines for rotor suspension, where the displacement sensors play a crucial role in detecting and controlling the rotor position. However, the traditional displacement sensors have the problems of high cost, large vol...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-11-01
|
| Series: | IET Electric Power Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/elp2.12499 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846151287238819840 |
|---|---|
| author | Longyuan Fan Zicheng Liu Haijiao Wang Dong Jiang Yu Chen |
| author_facet | Longyuan Fan Zicheng Liu Haijiao Wang Dong Jiang Yu Chen |
| author_sort | Longyuan Fan |
| collection | DOAJ |
| description | Abstract Active magnetic bearing (AMB) is a key technology in high‐speed rotating machines for rotor suspension, where the displacement sensors play a crucial role in detecting and controlling the rotor position. However, the traditional displacement sensors have the problems of high cost, large volume and poor reliability. To solve these problems, this paper proposes an innovative solution that utilises a recurrent neural network (RNN) to estimate the rotor displacement from the current in the AMB controller. The proposed method offers high‐quality prediction performance for the rotor displacement which is close to the high precision eddy current displacement sensors. The mathematical model of AMB is analysed to provide guidance in historical current data acquisition and design of RNN. The input dimensions and the architecture of the neural network are optimised to improve both prediction accuracy and computational complexity. Experimental results validate the effectiveness of the algorithm and demonstrate that the proposed method has high accuracy, robustness and generalisation ability. |
| format | Article |
| id | doaj-art-e0d4f6b38a6e42deb34186bc8ead8337 |
| institution | Kabale University |
| issn | 1751-8660 1751-8679 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Electric Power Applications |
| spelling | doaj-art-e0d4f6b38a6e42deb34186bc8ead83372024-11-27T17:03:06ZengWileyIET Electric Power Applications1751-86601751-86792024-11-0118111480149010.1049/elp2.12499A recurrent neural network‐based rotor displacement estimation method for eight‐pole active magnetic bearingLongyuan Fan0Zicheng Liu1Haijiao Wang2Dong Jiang3Yu Chen4School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan ChinaSchool of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan ChinaSchool of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan ChinaSchool of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan ChinaSchool of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan ChinaAbstract Active magnetic bearing (AMB) is a key technology in high‐speed rotating machines for rotor suspension, where the displacement sensors play a crucial role in detecting and controlling the rotor position. However, the traditional displacement sensors have the problems of high cost, large volume and poor reliability. To solve these problems, this paper proposes an innovative solution that utilises a recurrent neural network (RNN) to estimate the rotor displacement from the current in the AMB controller. The proposed method offers high‐quality prediction performance for the rotor displacement which is close to the high precision eddy current displacement sensors. The mathematical model of AMB is analysed to provide guidance in historical current data acquisition and design of RNN. The input dimensions and the architecture of the neural network are optimised to improve both prediction accuracy and computational complexity. Experimental results validate the effectiveness of the algorithm and demonstrate that the proposed method has high accuracy, robustness and generalisation ability.https://doi.org/10.1049/elp2.12499magnetic bearingsrecurrent neural nets |
| spellingShingle | Longyuan Fan Zicheng Liu Haijiao Wang Dong Jiang Yu Chen A recurrent neural network‐based rotor displacement estimation method for eight‐pole active magnetic bearing IET Electric Power Applications magnetic bearings recurrent neural nets |
| title | A recurrent neural network‐based rotor displacement estimation method for eight‐pole active magnetic bearing |
| title_full | A recurrent neural network‐based rotor displacement estimation method for eight‐pole active magnetic bearing |
| title_fullStr | A recurrent neural network‐based rotor displacement estimation method for eight‐pole active magnetic bearing |
| title_full_unstemmed | A recurrent neural network‐based rotor displacement estimation method for eight‐pole active magnetic bearing |
| title_short | A recurrent neural network‐based rotor displacement estimation method for eight‐pole active magnetic bearing |
| title_sort | recurrent neural network based rotor displacement estimation method for eight pole active magnetic bearing |
| topic | magnetic bearings recurrent neural nets |
| url | https://doi.org/10.1049/elp2.12499 |
| work_keys_str_mv | AT longyuanfan arecurrentneuralnetworkbasedrotordisplacementestimationmethodforeightpoleactivemagneticbearing AT zichengliu arecurrentneuralnetworkbasedrotordisplacementestimationmethodforeightpoleactivemagneticbearing AT haijiaowang arecurrentneuralnetworkbasedrotordisplacementestimationmethodforeightpoleactivemagneticbearing AT dongjiang arecurrentneuralnetworkbasedrotordisplacementestimationmethodforeightpoleactivemagneticbearing AT yuchen arecurrentneuralnetworkbasedrotordisplacementestimationmethodforeightpoleactivemagneticbearing AT longyuanfan recurrentneuralnetworkbasedrotordisplacementestimationmethodforeightpoleactivemagneticbearing AT zichengliu recurrentneuralnetworkbasedrotordisplacementestimationmethodforeightpoleactivemagneticbearing AT haijiaowang recurrentneuralnetworkbasedrotordisplacementestimationmethodforeightpoleactivemagneticbearing AT dongjiang recurrentneuralnetworkbasedrotordisplacementestimationmethodforeightpoleactivemagneticbearing AT yuchen recurrentneuralnetworkbasedrotordisplacementestimationmethodforeightpoleactivemagneticbearing |