Although composite structures popular in aerospace structures, due to complex failure modes, frequent inspection is required, increasing maintenance costs. Consequently, a structural health monitoring (SHM) system to monitor and pre-emptively detect damage is crucial. The Institute for Integrated and Intelligent Systems (IIIS) and the Advanced Design and Prototyping Technologies Institute (ADaPT) at Griffith University are planning to design and build a real-time SHM system utilizing Fiber Bragg Grating (FBG) sensors to obtain health data and use machine learning for real-time damage classification. We propose an experimental setup to generate ground truth neural network training. This includes methods to obtain data on material condition, an interrogation system for real-time monitoring, and machine learning for damage classification and remaining useful life (RUL) prediction.