Diagnosis and prognosis of aeroengines bearings Fault

Version 1.0.0 (36.9 KB) by BERGHOUT Tarek
Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning
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Updated 2 Oct 2023

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The package contains all the materials needed to reproduce the findings of our paper. The paper is published by MDPI Applied Sciences journal and its details are as follow.
Berghout, T.; Benbouzid, M. Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach. Appl. Sci. 2023, 13, 10916. https://doi.org/10.3390/app131910916
1) Please you need to download the dataset from original link provided by introductory paper (Please read the above paper to find out about the datset used).
2) Put the data in folders "RawData" for both experments.
3) Please run the files for each experiment as provided, in alphabetical order.
MATLAB Release Compatibility
Created with R2023a
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.0.0