Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images
This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/12/3405 |
| _version_ | 1797565134858616832 |
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| author | Diyar Khalis Bilal Mustafa Unel Mehmet Yildiz Bahattin Koc |
| author_facet | Diyar Khalis Bilal Mustafa Unel Mehmet Yildiz Bahattin Koc |
| author_sort | Diyar Khalis Bilal |
| collection | DOAJ |
| description | This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with a method based on Castigliano’s theorem. |
| first_indexed | 2024-03-10T19:07:36Z |
| format | Article |
| id | doaj.art-f85648c1195c49ba81207264ff503e00 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:07:36Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f85648c1195c49ba81207264ff503e002023-11-20T04:02:34ZengMDPI AGSensors1424-82202020-06-012012340510.3390/s20123405Realtime Localization and Estimation of Loads on Aircraft Wings from Depth ImagesDiyar Khalis Bilal0Mustafa Unel1Mehmet Yildiz2Bahattin Koc3Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, TurkeyFaculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, TurkeyFaculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, TurkeyFaculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, TurkeyThis paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with a method based on Castigliano’s theorem.https://www.mdpi.com/1424-8220/20/12/3405structural health monitoringload localizationload estimationdepth sensorartificial neural networkscastigliano’s theorem |
| spellingShingle | Diyar Khalis Bilal Mustafa Unel Mehmet Yildiz Bahattin Koc Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images Sensors structural health monitoring load localization load estimation depth sensor artificial neural networks castigliano’s theorem |
| title | Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images |
| title_full | Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images |
| title_fullStr | Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images |
| title_full_unstemmed | Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images |
| title_short | Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images |
| title_sort | realtime localization and estimation of loads on aircraft wings from depth images |
| topic | structural health monitoring load localization load estimation depth sensor artificial neural networks castigliano’s theorem |
| url | https://www.mdpi.com/1424-8220/20/12/3405 |
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