MATLAB codes for : "Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach".

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.本数据包包含复现本论文研究结论所需的全部材料。本文已由MDPI旗下的《Applied Sciences（应用科学）》期刊发表，具体信息如下：Berghout, T.; Benbouzid, M. 采用协同选择增量深度迁移学习方法实现高速航空轴承故障诊断与预测. Appl. Sci. 2023, 13, 10916. https://doi.org/10.3390/app131910916 1) 请从前述论文提供的原始链接下载本数据集（请阅读上述论文以了解所用数据集的相关信息）。 2) 将两次实验的数据均存入名为"RawData"的文件夹中。 3) 请按照字母顺序依次运行各实验对应的配套脚本文件。