CM-SAT addresses a relevant, high priority issue – Aviation Safety. The causal relationships learned in this project are directly applicable to the mitigation of aircraft aging, analysis and prediction of crew performance, anomaly detection, and the Verification and Validation of flight control systems. CM-SAT addresses several technical challenge cited by the NASA Aviation Safety Program (AvSP), including the challenges of Assurance of Flight Critical Systems, Discovery of Safety Issues, Vehicle Health Assurance, Crew-System Interactions and Decisions, Loss of Control Prevention, Mitigation, and Recovery, Engine and Airframe Icing, and Atmospheric Sensing & Mitigation. CM-SAT is most relevant to the System–wide Safety and Assurance Technologies (SSAT) project in that it directly addresses safety assurance by predicting risky conditions. However it also indirectly addresses issues in the Vehicle Safety Systems Technologies (VSST) project and Atmospheric Environment Safety Technologies (AEST) by providing general knowledge of the causal relationships on aircraft which lead to hazardous conditions.
CM-SAT analyzes large data repositories for causal relationships that can increase aviation safety, and thus it will be targeted for use by public agencies and private companies interested in aviation safety. The FAA Safety Management System (SMS) would also benefit from actionable safety information, a need which CM-SAT addresses. CM-SAT may be used by military agencies as well, including the Air Force. More broadly, CM-SAT is usable by any agency with large amounts of data that lends itself to causal analysis. Agencies interested in UAS Verification and Validation (V&V), such as ONR, are interested in analyzing collected data to predict performance. The distributed, scalable, heterogeneous causal learning technology that CM-SAT presents is also applicable to the domain of intelligence analysis.
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