The NASA Prognostic Model Package is a Python 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.
This is part of the wider Prognostics Python Packages (ProgPy) and is designed to be used with the Prognostics Algorithms Package.
pip3 install prog_models
See documentation here
Here is the directory structure for the github repository
src/prog_models/ - The prognostics model python package
docs/ - Project documentation
examples/ - Example Python scripts using prog_models
tests/ - Tests for prog_models
README.md - The readme (this file)
requirements.txt - Python library dependiencies required to be met to use this package
prog_model_template.py - Template for Prognostics Model
tutorial.ipynb - Tutorial (Juypter Notebook)
Use the following to cite this repository:
@misc{2022_nasa_prog_models,
author = {Christopher Teubert and Matteo Corbetta and Chetan Kulkarni and Katelyn Jarvis and Matthew Daigle},
title = {Prognostics Models Python Package},
month = December,
year = 2022,
version = {1.4},
url = {https://github.com/nasa/prog\_models}
}
The corresponding reference should look like this:
C. Teubert, C. Kulkarni, M. Corbetta, K. Jarvis, M. Daigle, Prognostics Model Python Package, v1.4, December 2022. URL https://github.com/nasa/prog_models.
Alternatively, if using both prog_models and prog_algs, you can cite the combined package as
C. Teubert, C. Kulkarni, M. Corbetta, K. Jarvis, M. Daigle, ProgPy Prognostics Python Packages, v1.4, October 2022. URL https://nasa.github.io/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. We would like to recognize Matthew Daigle and the rest of the team that contributed to the Prognostics Model Library for the contributions their work on the MATLAB library made to the design of prog_models.
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