NASA SBIR 2019-II Solicitation

Proposal Summary

 19-2- H6.01-3742
 Integrated Systems Health Management for Sustainable Habitats
 Reinforcement Learned Adversarial Agent for Active Fault Detection in Space Habitats
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Martin Defense Group, LLC
841 Bishop Street, Suite 1110
Honolulu, HI 96813
(808) 531-7001

PRINCIPAL INVESTIGATOR (Name, E-mail, Mail Address, City/State/Zip, Phone)
William Curran
4300 Wilson Blvd, Ste 350
Arlington, 22203 - 4167
(541) 590-5053

BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Ms. Audra White
841 Bishop Street
Honolulu, HI 96813 - 3908
(808) 695-6641

Estimated Technology Readiness Level (TRL) :
Begin: 3
End: 5
Technical Abstract (Limit 2000 characters, approximately 200 words)

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.

Duration: 24

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