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Matthew J. Daigle

Lead, Diagnostics & Prognostics Group
Research Computer Scientist


Matthew J. Daigle received the B.S. degree in Computer Science and Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, NY, in 2004, and the M.S. and Ph.D. degrees in Computer Science from Vanderbilt University, Nashville, TN, in 2006 and 2008, respectively.

From September 2004 to May 2008, he was a Graduate Research Assistant with the Institute for Software Integrated Systems and Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN. During the summers of 2006 and 2007, he was an intern with Mission Critical Technologies, Inc., at NASA Ames Research Center. From June 2008 to December 2011, he was an Associate Scientist with the University of California, Santa Cruz, at NASA Ames Research Center. Since January 2012, he has been with NASA Ames Research Center as a Research Computer Scientist. He has been the lead of the Diagnostics & Prognostics Group since September 2016. His current research interests include physics-based modeling, model-based diagnosis and prognosis, simulation, and hybrid systems.

Curriculum Vitae

Selected Publications

See for a full list of publications.

Open-source Software

Prognostics Model Library

The Prognostics Model Library is a modeling framework focused on defining and building models for prognostics (computation of remaining useful life) of engineering systems, and provides a set of prognostics models for select components developed within this framework, suitable for use in prognostics applications for these components. The library currently includes models for valves, pumps, and batteries. The Prognostics Model Library is implemented in MATLAB. The implementation consists of a set of utilities for defining a model (specifying variables, parameters, and equations), simulating the model, and embedding it within common model-based prognostics algorithms. A user can use existing models within the library or construct new models with the provided framework.

Prognostics Model Library at Github

Prognostics Algorithm Library

The Prognostics Algorithm Library is a suite of algorithms implemented in the MATLAB programming language for model-based prognostics (remaining life computation). It includes algorithms for state estimation and prediction, including uncertainty propagation. The algorithms take as inputs component models developed in Matlab, and perform estimation and prediction functions. The library allows the rapid development of prognostics solutions for given models of components and systems. Different algorithms can be easily swapped to do comparative studies and evaluations of different algorithms to select the best for the application at hand.

Prognostics Algorithm Library at Github

Generic Software Architecture for Prognostics

The Generic Software Architecture for Prognostics (GSAP) is a generic, extendable, flexible, modular C++ framework for applying prognostics technologies. GSAP manages top-level control, communications, logging, configuration, integration, and other general activities. A simple, standard interface is provided for integrating prognostics algorithms and models, minimizing the work required to deploy prognostics technologies. The standard interface allows for prognosers developed for GSAP to be reused anywhere GSAP is used.

GSAP at Github

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