The NASA Prognostic Package (ProgPy) is a python prognostics framework focused on building, using, and evaluating models and algorithms for prognostics (computation of remaining useful life) and health management of engineering systems, and provides a set of prognostics models for select components and prognostics algorithms developed within this framework, suitable for use in prognostics for these components. It also includes algorithms for state estimation and prediction, including uncertainty propagation.
ProgPy was the 2024 NASA Software of the Year!
ProgPy combines NASAs prog_models and prog_algs packages into a single python package, for simplicity and clarity.
pip3 install progpy
or
pip3 install progpy[datadriven]
to include dependencies for data driven models
See documentation here
Here is the directory structure for the github repository
src/progpy/ - The prognostics python package
examples/ - Example Python scripts using progpy
tests/ - Tests for progpy
README.md - The readme (this file)
prog_model_template.py - Template for Prognostics Model
state_estimator_template.py - Template for State Estimators
predictor_template.py - Template for Predictor
Use the following to cite this repository:
@misc{2024_nasa_progpy,
| author = {Christopher Teubert and Katelyn Jarvis Griffith and Matteo Corbetta and Chetan Kulkarni and Portia Banerjee and Matthew Daigle},
| title = {{ProgPy Python Prognostics Packages}},
| month = May,
| year = 2024,
| version = {1.7},
| url = {https://nasa.github.io/progpy}
| }
The corresponding reference should look like this:
C. Teubert, K. Jarvis Griffith, M. Corbetta, C. Kulkarni, P. Banerjee, M. Daigle, ProgPy Python Prognostics Packages, v1.7, May 2024. URL https://github.com/nasa/progpy.
ProgPy was created by a partnership of multiple organizations, working together to build a set of high-quality prognostic tools for the wider PHM Community. We would like to give a big thank you for the ProgPy community, especially the following contributing organizations:
- NASA's Diagnostics and Prognostics Group
- German Aerospace Center (DLR)
- Northrop Grumman Corporation (NGC)
- Vanderbilt University
The structure and algorithms of this package are strongly inspired by the MATLAB Prognostics Model Library, MATLAB Prognostics Algorithm Library, and the MATLAB Prognostics Metrics Library. We would like to recognize Matthew Daigle and the rest of the team that contributed to the Prognostics Models, Algorithms, and Metrics Libraries for the contributions their work on the MATLAB library made to the design of progpy.
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