A Supervised Scene Adaptive Model for Identifying Impact Load with Few Samples
Deep learning-based impact load identification technology for the next generation of large aircraft structures has garnered significant attention and has become one of the focal points in aircraft structural health monitoring. However, this technology relies on a large number of training samples and...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/10/3169 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850126177381908480 |
|---|---|
| author | Shengbao Bai Ji Yao Chenhui Huang Yuan Tian Zhigang Xiong Gang Chen Hu Sun |
| author_facet | Shengbao Bai Ji Yao Chenhui Huang Yuan Tian Zhigang Xiong Gang Chen Hu Sun |
| author_sort | Shengbao Bai |
| collection | DOAJ |
| description | Deep learning-based impact load identification technology for the next generation of large aircraft structures has garnered significant attention and has become one of the focal points in aircraft structural health monitoring. However, this technology relies on a large number of training samples and exhibits poor scalability. One of the current challenges in system-level multi-structure monitoring is how to construct deep learning models with a small number or even zero impact training samples, and improve the models’ ability to migrate between different structures. To address this challenge, a novel method for impact load identification using only a small number of samples, based on a supervised scene adaptive model, is proposed. The performance of the model is validated on real aircraft structures. For large and complex structures, the model can be applied to other similar structural areas or different structural areas by using samples from the baseline area for training. Then, a very small number of calibration samples from the migrated area can be used for calibration. The results demonstrate that the proposed model, calibrated with just a single sample, achieves 97.22% accuracy in impact location identification and 99.44% accuracy in energy identification under similar regional structural conditions. Under different structural region conditions, the location identification accuracy of the proposed model is 87.65%, while the energy identification accuracy remains at 98.85%. The position identification accuracy of the model is 91.98% under different impact energy level conditions, and the identification accuracy remains at 87.04% even under varying impact energy levels and structural region conditions. |
| format | Article |
| id | doaj-art-d90d3352819f47549bbf5ef5f84de1ae |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d90d3352819f47549bbf5ef5f84de1ae2025-08-20T02:33:58ZengMDPI AGSensors1424-82202025-05-012510316910.3390/s25103169A Supervised Scene Adaptive Model for Identifying Impact Load with Few SamplesShengbao Bai0Ji Yao1Chenhui Huang2Yuan Tian3Zhigang Xiong4Gang Chen5Hu Sun6National Key Laboratory of Structural Integrity, Aircraft Strength Research Institute of China, Xi’an 710065, ChinaNational Key Laboratory of Ship Structural Safety, China Ship Scientific Research Center, Wuxi 214082, ChinaSchool of Aerospace Engineering, Xiamen University, Xiamen 361005, ChinaNational Key Laboratory of Structural Integrity, Aircraft Strength Research Institute of China, Xi’an 710065, ChinaSchool of Aerospace Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Aerospace Engineering, Xiamen University, Xiamen 361005, ChinaSchool of Aerospace Engineering, Xiamen University, Xiamen 361005, ChinaDeep learning-based impact load identification technology for the next generation of large aircraft structures has garnered significant attention and has become one of the focal points in aircraft structural health monitoring. However, this technology relies on a large number of training samples and exhibits poor scalability. One of the current challenges in system-level multi-structure monitoring is how to construct deep learning models with a small number or even zero impact training samples, and improve the models’ ability to migrate between different structures. To address this challenge, a novel method for impact load identification using only a small number of samples, based on a supervised scene adaptive model, is proposed. The performance of the model is validated on real aircraft structures. For large and complex structures, the model can be applied to other similar structural areas or different structural areas by using samples from the baseline area for training. Then, a very small number of calibration samples from the migrated area can be used for calibration. The results demonstrate that the proposed model, calibrated with just a single sample, achieves 97.22% accuracy in impact location identification and 99.44% accuracy in energy identification under similar regional structural conditions. Under different structural region conditions, the location identification accuracy of the proposed model is 87.65%, while the energy identification accuracy remains at 98.85%. The position identification accuracy of the model is 91.98% under different impact energy level conditions, and the identification accuracy remains at 87.04% even under varying impact energy levels and structural region conditions.https://www.mdpi.com/1424-8220/25/10/3169structural health monitoringimpact load detectiontransfer learningscene adaptivedeep learning |
| spellingShingle | Shengbao Bai Ji Yao Chenhui Huang Yuan Tian Zhigang Xiong Gang Chen Hu Sun A Supervised Scene Adaptive Model for Identifying Impact Load with Few Samples Sensors structural health monitoring impact load detection transfer learning scene adaptive deep learning |
| title | A Supervised Scene Adaptive Model for Identifying Impact Load with Few Samples |
| title_full | A Supervised Scene Adaptive Model for Identifying Impact Load with Few Samples |
| title_fullStr | A Supervised Scene Adaptive Model for Identifying Impact Load with Few Samples |
| title_full_unstemmed | A Supervised Scene Adaptive Model for Identifying Impact Load with Few Samples |
| title_short | A Supervised Scene Adaptive Model for Identifying Impact Load with Few Samples |
| title_sort | supervised scene adaptive model for identifying impact load with few samples |
| topic | structural health monitoring impact load detection transfer learning scene adaptive deep learning |
| url | https://www.mdpi.com/1424-8220/25/10/3169 |
| work_keys_str_mv | AT shengbaobai asupervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT jiyao asupervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT chenhuihuang asupervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT yuantian asupervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT zhigangxiong asupervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT gangchen asupervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT husun asupervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT shengbaobai supervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT jiyao supervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT chenhuihuang supervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT yuantian supervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT zhigangxiong supervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT gangchen supervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples AT husun supervisedsceneadaptivemodelforidentifyingimpactloadwithfewsamples |