Monitoring damage in advanced composite structures using embedded fibre optic sensors

Fibre Bragg Grating (FBG) sensors are extremely sensitive to changes of strain, and are therefore an extremely useful candidate for Structural Health Monitoring (SHM) systems of composite structures. Sensitivity of FBGs to strain gradients originating from damage was observed as an indicator of initiation and propagation of damage in composite structures. To date there have been numerous research works done on distorted FBG spectra due to damage accumulation under controlled environments. Unfortunately, a number of related unresolved problems remain in FBG-based SHM systems development, making the present SHM systems unsuitable for real life applications. The work presented in this thesis highlights the application difficulties in using FBG for the SHM of advanced composite structures. The breakthrough technologies presented in this thesis resolve those major problems. As a solution to cope with complicated FBG responses, a novel signal processing approach was introduced using Artificial Neural Networks (ANN). To accommodate complete FBG spectral data into an ANN, a novel FBG data decoding system was developed. The Fixed FBG Filter Decoding System (FFFDS), along with an ANN was found to be an excellent tool for addressing real-time data input to in-situ FBG-based SHM systems. Several experimental studies have been used to investigate the decoding system and performance of the ANN for damage detection in composite structures. The proposed system has identified a delamination within 0.01% error levels. Even though previous work has used the distortion of FBG spectra to detect damage in composite structures, to date there is no clear definition for distortion of the FBG spectra. This is a major shortcoming in FBG-based SHM system development. This thesis presents two novel concepts, 'Distortion' and 'Distortion Index'. These have been used to define distortion of FBG spectra, and have been successfully used for damage identification and quantification in composite structures. A case study was also conducted to develop optimum the FBG sensor network for efficient damage detection in a composite structure. A detailed procedure was proposed for the optimization of FBG networks. The proposed optimization procedure extensively used finite element analysis (FEA), thereby eliminating expensive and time consuming prototype component testing for optimized sensor locations. Finally, developed decoding systems and the optimization methodology have been verified successfully using a representative sample. It was concluded that the breakthrough technologies developed under this thesis will exclude the major remaining problems associated with the development of SHM systems for advanced composite structures. Further, a few logical improvements were recommended for the development of next generation SHM systems.