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.
- M. Daigle and S. Sankararaman, "Predicting Remaining Driving Time and Distance of a Planetary Rover under Uncertainty," ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, to appear.
- M. Daigle, A. Bregon, X. Koutsoukos, G. Biswas, and B. Pulido, "A Qualitative Event-based Approach to Multiple Fault Diagnosis in Continuous Systems using Structural Model Decomposition," Engineering Applications of Artificial Intelligence, vol. 53, pp. 190-206, August 2016.
- C. Kulkarni, G. Gorospe, M. Daigle, and K. Goebel, "A Testbed for Implementing Prognostic Methodologies on Cryogenic Propellant Loading Systems," IEEE AUTOTESTCON 2014, September 2014. (best paper award)
- S. Sankararaman, M. Daigle, and K. Goebel, "Uncertainty Quantification in Remaining Useful Life Prediction using First-Order Reliability Methods," IEEE Transactions on Reliability, vol. 63, no. 2, pp. 603-619, June 2014.
- M. Daigle, A. Bregon, and I. Roychoudhury, "Distributed Prognostics Based on Structural Model Decomposition," IEEE Transactions on Reliability, vol. 63, no. 2, pp. 495-510, June 2014.
- A. Bregon, M. Daigle, I. Roychoudhury, G. Biswas, X. Koutsoukos, and B. Pulido, "An Event-based Distributed Diagnosis Framework using Structural Model Decomposition," Artificial Intelligence, vol. 210, pp. 1-35, May 2014.
- M. Daigle and C. Kulkarni, "A Battery Health Monitoring Framework for Planetary Rovers," 2014 IEEE Aerospace Conference, March 2014. (best paper award)
- E. Balaban, S. Narasimhan, M. Daigle, I. Roychoudhury, A. Sweet, C. Bond, and G. Gorospe, "Development of a Mobile Robot Test Platform and Methods for Validation of Prognostics-Enabled Decision Making Algorithms," International Journal of Prognostics and Health Management, vol. 4, no. 1, May 2013.
- M. Daigle and K. Goebel, "Model-based Prognostics with Concurrent Damage Progression Processes," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 43, no. 4, pp. 535-546, May 2013.
- M. Daigle, J. Boschee, M. Foygel, and V. Smelyanskiy, "Temperature Stratification in a Cryogenic Fuel Tank," AIAA Journal of Thermophysics and Heat Transfer, vol. 27, no. 1, pp. 116-126, January 2013.
- V. Osipov, M. Daigle, C. Muratov, M. Foygel, V. Smelyanskiy, and M. Watson, "Dynamical Model of Rocket Propellant Loading with Liquid Hydrogen," AIAA Journal of Spacecraft and Rockets, vol. 48, no. 6, pp. 987-998, November 2011.
- M. Daigle, I. Roychoudhury, S. Narasimhan, S. Saha, B. Saha, and K. Goebel, "Investigating the Effect of Damage Progression Model Choice on Prognostics Performance," Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011, pp. 323-333, September 2011. (won best paper award - theory paper)
- M. Daigle and K. Goebel, "A Model-based Prognostics Approach Applied to Pneumatic Valves," International Journal of Prognostics and Health Management, vol. 2, no. 2, August 2011.
- I. Roychoudhury, M. Daigle, G. Biswas, and X. Koutsoukos, "Efficient Simulation of Hybrid Systems: A Hybrid Bond Graph Approach," Simulation: Transactions of The Society for Modeling and Simulation International, vol. 87, no. 6, pp. 467-498, June 2011.
- M. Daigle, X. Koutsoukos, and G. Biswas, "An Event-based Approach to Integrated Parametric and Discrete Fault Diagnosis in Hybrid Systems," Transactions of the Institute of Measurement and Control, Special Issue on Hybrid and Switched Systems, vol. 32, no. 5, pp. 487-510, October 2010.
- M. Daigle, I. Roychoudhury, G. Biswas, X. Koutsoukos, A. Patterson-Hine, and S. Poll, "A Comprehensive Diagnosis Methodology for Complex Hybrid Systems: A Case Study on Spacecraft Power Distribution Systems," IEEE Transactions of Systems, Man, and Cybernetics, Part A, Special Section on Model-based Diagnosis: Facing Challenges in Real-world Applications, vol. 4, no. 5, pp. 917-931, September 2010.
- A. Moustafa, S. Mahadevan, M. Daigle, and G. Biswas, "Structural and Sensor Damage Identification using the Bond Graph Approach," Structural Control and Health Monitoring, vol. 17, no. 2, pp. 178-197, March 2010.
- M. Daigle, X. Koutsoukos, and G. Biswas, "A Qualitative Event-based Approach to Continuous Systems Diagnosis," IEEE Transactions on Control Systems Technology, vol. 17, no. 4, pp. 780-793, July 2009.
- M. Daigle, X. Koutsoukos, and G. Biswas, "Distributed Diagnosis in Formations of Mobile Robots," IEEE Transactions on Robotics, vol. 23, no. 2, pp. 353-369, April 2007.
See matthewjdaigle.com for a full list of publications.
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
Lead, Diagnostics & Prognostics Group
Research Computer Scientist
Intelligent Systems Division
NASA Ames Research Center
Mail Stop 269-3
Moffett Field, CA 94035