Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/s20123405 | SENSORS |
Keywords | DocType | Volume |
structural health monitoring,load localization,load estimation,depth sensor,artificial neural networks,castigliano's theorem | Journal | 20 |
Issue | ISSN | Citations |
12.0 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diyar Khalis Bilal | 1 | 0 | 1.35 |
| Mustafa Ünel | 2 | 154 | 20.71 |
| Mehmet Can Yildiz | 3 | 467 | 30.50 |
| Bahattin Koc | 4 | 59 | 8.13 |