SE-ResNet based disturbance identification algorithm for microthrust measurement system
Micronewton thrusters play a crucial role in the aerospace field, where the accuracy of micronewton thrust measurement is significantly impacted by environmental vibrations. However, existing methods for identifying vibration disturbances often fall short in terms of accuracy, especially under non-s...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-06-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0276866 |
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| _version_ | 1850108038194659328 |
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| author | Mingming Han |
| author_facet | Mingming Han |
| author_sort | Mingming Han |
| collection | DOAJ |
| description | Micronewton thrusters play a crucial role in the aerospace field, where the accuracy of micronewton thrust measurement is significantly impacted by environmental vibrations. However, existing methods for identifying vibration disturbances often fall short in terms of accuracy, especially under non-stationary working conditions. To address this issue, this study introduces a residual neural network (ResNet) to recognize impulse interferences and step signals and experimentally evaluates its performance. The Squeeze-and-Excitation (SE) module is incorporated to optimize the network, as it can adaptively enhance important features. This results in SE-ResNet having enhanced channel attention mechanisms, which improve the accuracy of disturbance recognition. The experimental results demonstrate that SE-ResNet can accurately identify the impulse, step, and steady-state responses of cantilever beams with an accuracy of 93.54% under the real-time control system for the microthrust measurement of cantilever beams. This is a notable improvement over the 88.91% accuracy achieved by ResNet alone. The proposed method shows great potential for providing a foundation for subsequent interference suppression against impulses and steps. |
| format | Article |
| id | doaj-art-d80c5eb3cd66488c970488373afa0bc3 |
| institution | OA Journals |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-d80c5eb3cd66488c970488373afa0bc32025-08-20T02:38:28ZengAIP Publishing LLCAIP Advances2158-32262025-06-01156065111065111-810.1063/5.0276866SE-ResNet based disturbance identification algorithm for microthrust measurement systemMingming Han0Key Laboratory of Micro-Inertial Instruments and Advanced Navigation Technology, Ministry of Education, School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, ChinaMicronewton thrusters play a crucial role in the aerospace field, where the accuracy of micronewton thrust measurement is significantly impacted by environmental vibrations. However, existing methods for identifying vibration disturbances often fall short in terms of accuracy, especially under non-stationary working conditions. To address this issue, this study introduces a residual neural network (ResNet) to recognize impulse interferences and step signals and experimentally evaluates its performance. The Squeeze-and-Excitation (SE) module is incorporated to optimize the network, as it can adaptively enhance important features. This results in SE-ResNet having enhanced channel attention mechanisms, which improve the accuracy of disturbance recognition. The experimental results demonstrate that SE-ResNet can accurately identify the impulse, step, and steady-state responses of cantilever beams with an accuracy of 93.54% under the real-time control system for the microthrust measurement of cantilever beams. This is a notable improvement over the 88.91% accuracy achieved by ResNet alone. The proposed method shows great potential for providing a foundation for subsequent interference suppression against impulses and steps.http://dx.doi.org/10.1063/5.0276866 |
| spellingShingle | Mingming Han SE-ResNet based disturbance identification algorithm for microthrust measurement system AIP Advances |
| title | SE-ResNet based disturbance identification algorithm for microthrust measurement system |
| title_full | SE-ResNet based disturbance identification algorithm for microthrust measurement system |
| title_fullStr | SE-ResNet based disturbance identification algorithm for microthrust measurement system |
| title_full_unstemmed | SE-ResNet based disturbance identification algorithm for microthrust measurement system |
| title_short | SE-ResNet based disturbance identification algorithm for microthrust measurement system |
| title_sort | se resnet based disturbance identification algorithm for microthrust measurement system |
| url | http://dx.doi.org/10.1063/5.0276866 |
| work_keys_str_mv | AT mingminghan seresnetbaseddisturbanceidentificationalgorithmformicrothrustmeasurementsystem |