INTEGRATION OF INTELLIGENT HEALTH MONITORING SYSTEMS INTO INFLATABLE HYBRID STRUCTURES

Human missions require an integrated and reliable set of systems to safely and efficiently live and work in space. NASA's technology gap is to develop materials for an inflatable structure, to increase multi-functionality and reduce mass and cost suitable for long-term exposure to planetary environments. Piezoelectric materials and fiber optic sensors are ideal candidates for health monitoring, including leak detection, damage resistance or tolerance, vibration control, micrometeoroid orbital debris (MMOD) protection, smoke detection, and radiation protection. However, there are various challenges associated as we integrate the structural health management system in inflatable structures: 1. Reliability challenges such as restoring structural integrity most effectively, either repair or self-repair. 2. Detecting and diagnosing the damage and structural residual life. 3. Sustainment technologies (environment and health monitoring, repair). This paper highlights the application of piezoelectric materials and fiber optic sensors and their integration techniques in the inflatable structures for the Structural Health Management System. The finite element analysis model of three types of pressure vessels was designed, such as cylindrical, toroid, and spherical shape, using ABAQUS software. Further structural analysis will be performed to compare the model with fewer penetrations and multiple penetrations. The accessible literature established specific limitations for the pressurized module, shell thickness, and structure size. The part of this study was establishing material comparison criteria, studying interfaces, connections, leakage modes, a list of equipment for installation, and packaging techniques. After investigating the existing self-healing materials, a specific criterion was established to set the material's physics to simulate and identify integration parameters. The experimental part of this study primarily involves impact location detection using AI and machine learning algorithms. A scaled model setup was created using a pneumatic fender to mimic an inflatable space structure, and numerous PZT (Lead Zirconate Titanate, a type of piezoceramic material) sensors were attached to the scaled model using adhesive. The impact was simulated on the scaled model using an object at various locations throughout the geometry. Based on the data acquired by the PZT sensors and the Data Acquisition System during the monitoring of impact localization, analysis was performed by applying machine learning algorithms to the data for the Impact detection on the scaled model (pneumatic fender). Further, a command control unit layout and the risk matrix through response strategies are also designed as a part of this research.