Reinforcement Learned Adversarial Agent for Active Fault Detection in Space Habitats

Navatek proposes to develop a fault prediction and detection solution that improves NASArsquo;s ability to reveal latent, unknown conditions while also improving its detection time and reducing the rate of false positive and negative detections of known conditions that would lead to failure of the life sustainment system. Our approach feeds historical and real-time sensor data to a digital twin of the life sustainment system, which is a digital simulation of the entire functioning system and its environment. This digital twin is used by a reinforcement learning adversarial agent (RLAA) to simulate many possible scenarios into the future. The RLAA autonomously learns the environmental and system perturbations that lead to faults in its simulations, thus providing a method for prediction. These predictions are continuously compared against new incoming data to detect faults and further improve the digital twinrsquo;s accuracy.Navatekrsquo;s experience in physics-based modeling and simulation of environmental conditions and power flows is directly relevant to building the digital twins needed to manage the system health of space habitats. For Phase I and II, we will limit the scope of our digital twins to structural integrity and HVAC systems, to include their power supplies, leveraging data from pressure, flowrate, UV, temperature, and current/voltage sensors. Navatek is also uniquely capable of designing and rapidly prototyping low-cost inflatable structures. If selected for Phase II, we propose to construct a surrogate inflatable space habitat as an experimental apparatus with which to perform rapid, low-cost validation of the digital twinrsquo;s fault prediction and detection ability.