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.
Highlights
Structural Health Monitoring (SHM) has been an increasingly important technology in monitoring the structural integrity of composite materials used in the aerospace industry
Autoencoders were used to extract informative data from the depth images at a much smaller scale. subsequently, the proposed artificial neural networks (ANN) were trained using the training set described in Section 4.1, and their performance was evaluated and compared with a modified version of Castigliano’s theorem (The readers can refer to the Appendix A for the details of this algorithm.) on the constructed test set
A robust structural health monitoring system based on depth imaging and artificial neural networks for localization and estimation of bending and twisting loads acting on an aircraft wing in real time is proposed
Summary
Structural Health Monitoring (SHM) has been an increasingly important technology in monitoring the structural integrity of composite materials used in the aerospace industry. Because airframes operate under continuous external loads, they will be exposed to large deflections that may adversely affect their structural integrity. Critical components, such as fuselage and wings, should be monitored to ensure long service life. These components are designed to withstand different types of loading conditions, such as bending, torsion, tension, and compression, among others, a robust SHM system will be extremely valuable for the aerospace industry for realtime monitoring of loads. A large number of them need to be installed if one needs to monitor the whole wing due to their small size
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