Design, implementation and experimentation of an innovative platform based on optical FBG sensors for aircraft monitoring and flight testing

This Thesis presents the design, development, and validation (through ground and flight testing) of an innovative data acquisition and processing system utilizing optical sensors for real-time health monitoring of aircraft systems and structures. The primary objective is to exploit the capabilities of Fiber Bragg Grating (FBG) sensors, designing the acquisition architecture and the processing algorithms, in order to acquire the FBG raw measurements and transforming them into useful information for flight testing, real-time monitoring and advanced maintenance purposes. The UAV used for this research, Anubi, is a fixed-wing aircraft with an extensive instrumentation setup, including FBG sensors integrated into the wings for strain measurements, along with additional optical sensors for thermal, structural and system monitoring. Model-based and data-driven approaches were adopted to develop and implement various data processing algorithms. A reduced-order structural model of the wing was designed and implemented, in order to exploit optical measurements for a variety of purposes (both in real-time and off-line applications). The concept was validated by comparing simulation results with experimental data, demonstrating accurate replication and prediction of the real aircraft behavior, with major implications on structures, on-board systems and flight dynamics monitoring. Ground tests were designed and conducted to perform a preliminary assessment of the monitoring logics and a parametric identification for the models that were realized. Previous flight tests were analyzed to validate the systems developed for this study. Previous tests used a custom, low-frequency data acquisition system that highlighted the need for improved sampling rates to fully leverage the FBG sensors advantages. Therefore, a new in-flight acquisition architecture was designed and implemented to address this limitation, enabling a more efficient data collection for in-flight testing. Test flights were subsequently conducted to prove the capabilities of the new architecture, processing algorithms and models developed for this work. Namely, the system successfully estimated the total aircraft weight through lift measurements derived from wing deformation data. During maneuvers, it accurately assessed the increase in lift and corresponding load factor, demonstrating the system capabilities for a wide range of advanced monitoring and sensing purposes. The processed data revealed consistent and reliable performance of the new architecture. Temperature compensation methods proved effective across varied external conditions, reinforcing the robustness of the monitoring system. Data processing was carried out using specialized routines designed to acquire data in real-time, simulate live acquisitions, execute the monitoring algorithms and calibrate the developed models. Future work will focus on further refinement of the system and exploration of additional functionalities, along with improvements of the sensors configuration. The results of the flight tests proved the efficacy of the FBG-based monitoring system in providing high-fidelity information. The system has the potential to enhance aircraft monitoring, support advanced maintenance strategies, and offer a reliable backup or alternative to traditional instrumentation. The data processing methodologies serve as a comprehensive reference for future improvements, providing an initial technology demonstrator and proof of concept.