QSI PROPOSES TO FIELD A "FLEXIBLE" ISHM SOLUTION FOR SUSTAINABLE HABITATS UTILIZING THE TEAMS TOOLSET AND C…

Public award description

QSI PROPOSES TO FIELD A "FLEXIBLE" ISHM SOLUTION FOR SUSTAINABLE HABITATS UTILIZING THE TEAMS TOOLSET AND CONCOMITANT MODEL-BASED AND DATA-DRIVEN DIAGNOSTIC/PROGNOSTIC REASONING TECHNOLOGIES TO ENABLE THE HABITAT CREW AND GROUND SUPPORT PERSONNEL TO OBTAIN CRUCIAL ALERTS THAT AFFECT THE OPERATION OF CRITICAL HABITAT SUBSYSTEMS. AUTOMATED HEALTH ASSESSMENT, CREW ALERTS AND FUTURE DEGRADATION ESTIMATES WILL BE GENERATED TO FACILITATE CORRECTIVE ACTIONS IN THE FACE OF OFF-NOMINAL AND FAILURE CONDITIONS. THE ISHM SOLUTION WOULD REDUCE THE COGNITIVE LOAD ON THE CREW GIVEN THE ABUNDANCE OF INFORMATION THAT HAS TO BE REASONED UPON IN A TIMELY FASHION. THIS WILL BE CRITICAL FOR IMPROVING MISSION AND SYSTEM SAFETY. THE SOLUTION WILL UTILIZE HABITAT'S REAL-TIME SYSTEM HEALTH ASSESSMENT, ANOMALY AND FAILURE DETECTION, MACHINE LEARNING AND ACTIVE LEARNING TECHNIQUES TO PROVIDE CLEAR AND CONCISE DECISION SUPPORT TO IMPROVE SITUATIONAL AWARENESS AND PERFORM PROACTIVE CORRECTIVE ACTIONS. THE SOLUTION PROVIDES THE ABILITY TO REPORT AND INCORPORATE PREVIOUSLY UNDISCOVERED ANOMALIES THROUGH A VISUALLY INTUITIVE ACTIVE LEARNING INTERFACE. QSI'S HYBRID MODELING CONCEPT LEVERAGES DOMAIN INFORMATION FROM VARIOUS KNOWLEDGE SOURCES SUCH AS SYSML, VISIO, ETC. TO AUGMENT ITS DATA-DRIVEN MODELS WITH SYSTEM-LEVEL INTERDEPENDENCIES, WHICH PROVIDE CRITICAL INSIGHT INTO THE SYSTEM WHEN NEW ANOMALIES NEED TO BE IDENTIFIED BY THE HUMAN-IN-THE-LOOP. ADDITIONALLY, THE TEAMS FRAMEWORK WILL ENABLE INTEGRATION OF THIRD-PARTY MACHINE LEARNING MODULES TO LEVERAGE BEST-IN-CLASS ANOMALY DETECTION TECHNIQUES FOR AN INTEGRATED SOLUTION. THESE TECHNOLOGIES WOULD REDUCE THE COST AND RISK OF HABITAT OPERATIONS, ACROSS ALL ITS PHASES: DEVELOPMENT, FLIGHT UNIT PRODUCTION, LAUNCH, AND OPERATIONS.