NASA SBIR 2018-I Solicitation

Proposal Summary

 18-1- H6.01-1038
 Integrated System Health Management for Sustainable Habitats
 Anomaly Detection via Topological Feature Map
SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Stottler Henke Associates, Inc.
1650 South Amphlett Boulevard, Suite 300
San Mateo , CA 94402-2516-2516
(650) 981-2700

Principal Investigator (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dr. Sowmya Ramachandran
1650 South Amphlett Boulevard, Suite 300 San Mateo, CA 94402 - 2516
(650) 931-2716

Business Official (Name, E-mail, Mail Address, City/State/Zip, Phone)
Nate Henke
1650 South Amphlett Boulevard, Suite 300 San Mateo, CA 94402 - 2513
(650) 931-2719
Estimated Technology Readiness Level (TRL) :
Begin: 1
End: 4
Technical Abstract

We propose a machine-learning technology that significantly expands NASA’s real-time and offline ISHM capabilities for future deep-space exploration efforts. Our proposed system, Anomaly Detection via Topological fEAture Map (AD-TEAM), will leverage a Self-Organizing Map (SOM)-based architecture to produce high-resolution clusters of nominal system behavior. What distinguishes AD-TEAM from more common clustering techniques (e.g., k-means) in the ISHM-space is that it maps high-dimensional input vectors to a 2D grid while preserving the topology of the original dataset. The result is a ‘semantic map’ that serves as a powerful visualization tool for uncovering latent relationships between features of the incoming points. Thus, beyond detecting known and unknown anomalies, AD-TEAM will also enable space crew to semantically characterize the clusters discovered. In doing so, personnel will better understand how faults propagate throughout a system, the transitional states of subsystem degradation over time, and the dominant features (and their relationships) of subsystem behavior. In addition to analyzing single subsystem datasets, we also propose to cross-correlate subsystems in order to capture the cascading effect of faults from one subsystem to another, as well as discover latent relationships between subsystems.  Such analysis would significantly aid in the maintenance and overhauling activities of NASA’s deep-space missions.

Potential NASA Applications

One transition target is Orbital ATK, which has expressed interest in AD-TEAM as a potential integration into their ISHM systems. Orbital ATK has been chosen for innovation under NASA’s Next Space Technologies for Exploration Partnerships (NextSTEP-2) program, so a partnership presents opportunity for integration into a real NASA space technology.  Another target is the Sustainability Base at ARC for us to test AD-TEAM on their datasets, and for them to adapt our research to their ISHM tools.

Potential Non-NASA Applications

We have begun conversations with Derek R. DeVries, an Orbital ATK Sr. Fellow Discipline Owner for Propulsion System’s Avionics and Control Disciplines. In an official letter of endorsement (attached to this proposal), he believes AD-TEAM has good potential for the PHM systems of Orbital ATK’s Avionics and Control Division. We plan to grow this relationship with Orbital ATK’s Avionics and Control Division through Phase I and Phase II.

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