**Diyar Khalis Bilal 1,2, Mustafa Unel 1,2,\*, Mehmet Yildiz 1,2 and Bahattin Koc 1,2**


Received: 16 May 2020; Accepted: 12 June 2020; Published: 16 June 2020

**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.

**Keywords:** structural health monitoring; load localization; load estimation; depth sensor; artificial neural networks; castigliano's theorem
