Shape and Load Estimation using Fiber Bragg Grating Sensors for Structural Health Monitoring of Aircraft Structures

Abstract

Aerospace structures are exposed to severe loading and environmental conditions, which necessitates constant inspection and maintenance to improve safety and reliability. Currently, the aerospace industry largely depends on Nondestructive Evaluation (NDE) methods for the detection and characterization of structural damages. These methods are off-line, and hence, the aircraft has to be removed from normal operation and often needs to be disassembled for inspection. As a result, conventional NDE methods have moved towards a new concept, structural health monitoring (SHM), which provides online structural integrity information of the structure and is intended to enhance safety and reliability while decreasing downtime, operating, and maintenance costs. Fiber optic sensor technology, in particular, fiber Bragg grating (FBG) sensors, have become increasingly popular for SHM applications of aerospace engineering due to its unique superior characteristics in terms of small size, electromagnetic immunity, multiplexing capability, high bandwidth, and the possibility to be embedded within the material. Under the scope of the investigations carried out in this thesis, FBG sensors have been explored for the shape estimation and load monitoring applications of aircraft structures. A modal approach for shape estimation is investigated for the purpose of real-time health monitoring, control, and condition assessment of lightweight aerospace structures. The methodology implements the use of FBG sensors to obtain strain measurements from the target structure and to estimate the displacement field. A strain to displacement transformation matrix is derived using mode shapes to estimate the global displacement of a structure from measured discrete strain data. The number of FBG sensors and sensor layout for the shape estimation is optimized using a genetic algorithm. Static and dynamic displacement experiments are conducted on an aluminum plate to verify the algorithm. To test the performance of the algorithm for a large-scale application, the wing shape of an all-metal turboprop aircraft is estimated during static loading on-ground. The experimental results show that the proposed algorithm, along with strain data measured using FBG sensors, could estimate the real-time shape of aerospace structures. The second application demonstrates the feasibility of estimating the aircraft landing gear structural loads using strain data from FBG sensors and a machine learning algorithm. Gaussian process regression is used for the prediction of loads on the landing gear components such as axle, side brace, drag brace, and shock strut. Using this method, the operational load on several components of landing gear can be estimated with high accuracy using strain measured from the axle and common aircraft parameters such as wheel speed, shock absorber pressure, shock absorber displacement, and aircraft attitude, descent velocity and acceleration. To train the model, comprehensive measurement data from drop tests are used. The load estimation results for unseen drop test data show that the proposed method can be used to predict the load pattern of aircraft landing gears. Apart from 2D shape estimation, linear displacement measurement is important for the control and health monitoring applications in the aerospace industry. In this work, an improved FBG based displacement sensor is designed for reliable operation in harsh environments. The calibration and field trial results demonstrated higher sensitivity with excellent linearity and temperature compensation. This sensor can be utilized for long-range and high endurance displacement measurement applications.