NASA Logo, National Aeronautics and Space Administration

+NASA Home

+Ames Home

Matthew Daigle Gives Invited Lecture at University of Maryland, College Park
Intelligent Systems Division Banner

Matthew Daigle Gives Invited Lecture at University of Maryland, College Park

Matthew Daigle delivered an invited lecture on “Model-Based Prognostics” at the University of Maryland (UMD), College Park, as part of UMD’s graduate course “Prognostics and Health Management”. Matt’s lecture focused on the estimation of system health and the prediction of critical events such as the end of usable life. In particular, he delineated model-based prognostics, which establishes a formal mathematical framework for defining prognostics problems and algorithms for solving them, through the use of system models. The talk further presented a mathematical framework for prediction under uncertainty, a modeling framework for prognostics, and Open Source software implementing these concepts. The lecture was attended by 15 on-campus students and 20 off-campus students, many of whom are from industry.

BACKGROUND: Dr. Matthew Daigle is the Group Lead for the Diagnostics and Prognostics Group in the Discovery and Systems Health research area. His research is focused on physics-based modeling, model-based diagnostics, and model-based prognostics. The prognostics framework that was presented during the lecture has been developed over a number of years through different projects, initially focusing on prognostics of components within an aircraft, ground support equipment, and rovers. The work has evolved to prognostics of the vehicle (for example, predicting remaining flying time of a battery-powered electric aircraft) to prognostics at the system level. Most recently, prognostics has been extended to the monitoring and prediction of safety for the National Airspace System (NAS), as defined through a set of safety metrics, safety thresholds, safety margins, and unsafe events. The future occurrence of unsafe events and the future safety assessment of the NAS can be computed with a model-based prognostics framework.

NASA PROGRAM FUNDING: The work presented was funded by the Shadow-Mode Assessment with Realistic Technologies for the National Airspace System (SMART-NAS) project within the Airspace Operations and Safety Program (AOSP) under the Aeronautics Research Mission Directorate (ARMD).

POINT OF CONTACT: Matthew Daigle, matthew.j.daigle@nasa.gov

First Gov logo
NASA Logo - nasa.gov