Multi-Modal SHM of Inflatable Habitats via Embedded Fiber Optics Sensing

Luna is proposing to add new distributed acoustic sensing capabilities to embedded fiber optic sensors that have been proven to sense creep in inflatable structures. The team will apply Artificial Intelligence (AI) / Machine Learning (ML) algorithms to the strain and acoustic signals to automatically localize impacts and leaks, sense creep in the restraint layer materials, and detect anomalies in real-time. Phase I will focus on applying distributed acoustic sensing (DAS) to webbing materials to prove the feasibility of this new sensing modality. Strain profile data and acoustic data from the fiber optic sensors will serve as input to prototype AI/ML algorithms that will extract features for tracking and modeling creep trends, damage, and remaining life. Tensile testing of flight-representative webbing with embedded fiber optics and sensor egress designs will prove the ruggedness and quantify strength knockdown factor that is critical for acceptance in future flight vehicles. Folding and packing studies will be performed to further assess feasibility of the sensors for launch and deployment. Designs for space hardened sensors, modules, cables, and ruggedized interrogator will be identified for implementation in Phase II. The Phase I will culminate with a demonstration of impact and leak detection and localization using data from an instrumented Vectran inflatable test article. Phase II will deliver prototype software and hardware for further evaluation by NASA and/or industry partners. Phase III will transition the sensing and AI/ML technology to space stations and habitat modules.