A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation

The address of the paper is at（https://link.springer.com/article/10.1007/s10489-023-04777-0）

Citations

If you are using the code/model/data provided here in a publication, please consider citing our paper:

[1]Wang Y, Wang Y. A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation[J]. Applied Intelligence, 2023: 1-18.
@article{wang2023denoising,
  title={A denoising semi-supervised deep learning model for remaining useful life prediction of turbofan engine degradation},
  author={Wang, Youming and Wang, Yue},
  journal={Applied Intelligence},
  pages={1--18},
  year={2023},
  publisher={Springer}
}

Get Start

文件夹中包含模型文件,数据集，实验结果以及其他相关文件 The folder contains model files, data sets, experimental results, and other relevant files

Result

C-MAPSS：Test results of this model on C-MAPSS data set.

Datsets

We used the C-MAPSS dataset, which contains four subsets FD001, FD002, FD003 and FD004.Refer to the description in the article for specific introduction.

Test results on dataset C-MAPSS:

Reference and Acknowledgments

When building our code We referenced the repositories as follow:
https://github.com/ddrrrr/projectRUL