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Diagnostics and Prognostics Group Release Open Source Prognostics Toolboxes
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Diagnostics and Prognostics Group Release Open Source Prognostics Toolboxes

Research software entitled “Prognostics Model Library” and “Prognostics Algorithm Library”, by Matthew Daigle, have been released under a NASA Open Source Agreement. Releases can be found on the NASA Github site:


The Prognostics Model Library is a modeling framework implemented in MATLAB and focused on defining and building models for prognostics 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 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 component models developed in Matlab as inputs 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 various algorithms in order to select the best algorithm for the application at hand.

BACKGROUND: Prognostics is an emerging systems engineering discipline focused on predicting end-of-life of components and systems. The Prognostics Algorithm Library currently includes generic prognostic algorithms and models for batteries, valves, and pumps. The models and algorithms had all been previously published in conference and journal papers as pseudo-code and mathematical formula and were originally developed under the auspices of the Aeronautics projects’ Integrated Vehicle Health Management and System-wide Safety Assurance Technologies projects. The implementation in the library 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.

NASA PROGRAM FUNDING: System-wide Safety and Assurance Technologies (SSAT) project, Aeronautics Research Mission Directorate (ARMD); Advanced Ground Systems Maintenance (AGSM) project, Human Exploration and Operations Mission Directorate (HEOMD)

TEAM: Matthew Daigle and Kai Goebel

POINT OF CONTACT: Matthew Daigle,

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