SBIR-STTR Award

Navatek proposes to develop a fault prediction and detection solution that improves NASA’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 twin’s accuracy. Navatek’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 twin’s fault prediction and detection ability. Potential NASA Applications (Limit 1500 characters, approximately 150 words) The proposed effort will lead to valuable contributions to active fault detection in hazardous environments. The reinforcement learning adversarial agent architecture we are proposing would significantly expand the operational envelope of NASA space environment research by enabling faults to be accurately predicted and prevented, saving lives and infrastructure in the following applications. ? Habitation Systems ? Power Plant Operations ? Flight Controllers ? Spaceflight Missions Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words) Reinforcement learning adversarial agents have non-NASA commercial applicability for fault detection in advanced automated systems. Such systems are necessarily complex, the volumes of sensor data are large and not well-suited for human-only monitoring, and the consequences of system failure are severe. Examples: ? Aircraft ? Robot Exploration in Hazardous Environments ? Unmanned Underwater Vehicles

NASA intends on building a U.S.-led physical base on the moon capable of supporting human life. This “Sustainability Base” will require habitat environmental control and life sustainment systems. Such systems are necessarily complex, the volumes of sensor data are large and not well-suited for human-only monitoring, and the consequences of system failure are severe. Thus, to sustain optimal performance and avoid catastrophic failures, NASA seeks a health management system that will continuously monitor and quickly and accurately diagnose faulty system behavior. Navatek proposes to develop a fault prediction and detection solution that improves NASA’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 systems, which is a digital simulation of the entire functioning system and its environment. This digital twin is used by a reinforcement learning adversarial agent to simulate many possible scenarios into the future. The adversarial agent 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 twin’s accuracy. If successful, our proposed solution will provide NASA with an early warning system for faults in the life sustainment systems on space habitats, particularly integrity of the structural and HVAC systems. We will also show how our digital twin and reinforcement learning adversarial agent approach can be generalized to monitor other space habitat systems. Potential NASA Applications (Limit 1500 characters, approximately 150 words) If successful, the active fault detection architecture we are developing would significantly expand the operational envelope of NASA space environment research by enabling faults to be accurately predicted and prevented by a fault management system, saving lives and infrastructure. Within NASA’s projects this work would contribute to the Next Space Technologies for Exploration Partnerships-2 (NextSTEP-2) program by improving the safety of deep space exploration capabilities that support extensive human spaceflight missions. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words) Non-NASA applications of this work includes and sustainment analytics. Sustainment analytics is important in many commercial applications for health monitoring, like autonomous vehicles, power plant, wind turbines, etc.Maintenance costs for these applications can easily exceed the procurement costs.Our active fault detection framework can predict potential faults and prevent catastrophic failures.