New CMAPSS dataset (2021): Preparation & application example

MATLAB codes from data preparation to RUL prediction.
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Updated 29 Dec 2022

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These codes are dedicated to the preparation of the New C-MAPSS dataset and the reconstruction of a remaining useful life (RUL) machine learning model. To understand the codes, please read the following paper carefully. Please comment and share your questions and experiences with us.
Please provide a full citation of our paper as:
AMA Style
Berghout T, Mouss M-D, Mouss L-H, Benbouzid M. ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions. Aerospace. 2023; 10(1):10. https://doi.org/10.3390/aerospace10010010
Chicago/Turabian Style
Berghout, Tarek, Mohamed-Djamel Mouss, Leïla-Hayet Mouss, and Mohamed Benbouzid. 2023. "ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions" Aerospace 10, no. 1: 10. https://doi.org/10.3390/aerospace10010010
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Also, if you are new to RUL prediction, you might be interested in our Beginners Guide at:https://www.researchgate.net/publication/359578507_A_Systematic_Guide_for_Predicting_Remaining_Useful_Life_with_Machine_Learning
Please cite this comprehanssive paper as:
AMA Style
Berghout T, Benbouzid M. A Systematic Guide for Predicting Remaining Useful Life with Machine Learning. Electronics. 2022; 11(7):1125. https://doi.org/10.3390/electronics11071125
Chicago/Turabian Style
Berghout, Tarek, and Mohamed Benbouzid. 2022. "A Systematic Guide for Predicting Remaining Useful Life with Machine Learning" Electronics 11, no. 7: 1125. https://doi.org/10.3390/electronics11071125
MATLAB Release Compatibility
Created with R2018b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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N_CMAPSS_MATHWORKS/ML

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N_CMAPSS_MATHWORKS/Uncertainty

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Version Published Release Notes
1.0.2

adding new references for more state of the art

1.0.1

Citation updated

1.0.0