Ares I-X Ground Diagnostic PrototypeThe automation of pre-launch diagnostics for launch vehicles offers three potential benefits: improving safety, reducing cost, and reducing launch delays. The Ares I-X Ground Diagnostic Prototype demonstrated anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage Thrust Vector Control and for the associated ground hydraulics while the vehicle was in the Vehicle Assembly Building at Kennedy Space Center (KSC) and while it was on the launch pad. The prototype combines three existing tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool from Qualtech Systems Inc. for fault isolation and diagnostics. The second tool, SHINE (Spacecraft Health Inference Engine), is a rule-based expert system that was developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification, and used the outputs of SHINE as inputs to TEAMS. The third tool, IMS (Inductive Monitoring System), is an anomaly detection tool that was developed at NASA Ames Research Center. The three tools were integrated and deployed to KSC, where they were interfaced with live data. This paper describes how the prototype performed during the period of time before the launch, including accuracy and computer resource usage. The paper concludes with some of the lessons that we learned from the experience of developing and deploying the prototype.
Document ID
20100027332
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Mark Schwabacher (Ames Research Center Mountain View, United States)
Rodney Martin (Ames Research Center Mountain View, United States)
Robert Waterman (Kennedy Space Center Merritt Island, United States)
Rebecca Oostdyk (ASRC Aerospace Corporation Greenbelt, MD, United States)
John Ossenfort (Stinger Ghaffarian Technologies (United States) Greenbelt, United States)
Bryan Matthews (Stinger Ghaffarian Technologies (United States) Greenbelt, United States)
Date Acquired
August 24, 2013
Publication Date
April 20, 2010
Publication Information
Publication: AIAA Infotech@Aerospace 2010
Publisher: American Institute of Aeronautics and Astronautics
Sponsors: American Institute of Aeronautics and Astronautics
Funding Number(s)
WBS: 425180.04.02.02
Distribution Limits
Public
Copyright
Public Use Permitted.
Technical Review
NASA Peer Committee
Keywords
Machine LearningRocket EnginesAlgorithmsInternational Space StationSpace ShuttleJet Propulsion LaboratoryHydraulicsLaunch PadLaunch VehiclesNASA Ames Research CenterFault Detection and IsolationAnomaly Detection SystemThrust Vector Control