foryichuanqi / ADVEI-Paper-2023.11-Zero-shot-fault-diagnosis-by-attribute-fusion-transfer

Zero-shot fault diagnosis on the Tennessee–Eastman process by attribute fusion transfer. Paper: Attribute fusion transfer for zero-shot fault diagnosis

ADVEI-Paper-2023.11-Zero-shot-fault-diagnosis-by-attribute-fusion-transfer

Attribute fusion transfer for zero-shot fault diagnosis on the Tennessee–Eastman process.

Paper: Attribute fusion transfer for zero-shot fault diagnosis

The website of the paper：https://www.sciencedirect.com/science/article/pii/S1474034623003324?dgcid=author

Easy to reproduce all details of our paper successfully

The downloaded compressed package can reproduce our method.To make code easy to run successfully, we debug the files carefully. Generally speaking, if environments are satisfied, you can directly run all the xxx.py files inside after decompressing the compressed package without changing any code.

(1) Download and rename xxx.zip to AFT.zip (Rename to avoid errors caused by long directories)

(2) Unzip AFT.zip

(3) Run any xxx.py directly

Paper of Code and Citation

(1) To better understand our code, please read our paper.

Paper: Attribute fusion transfer for zero-shot fault diagnosis

The website of the paper：https://www.sciencedirect.com/science/article/pii/S1474034623003324?dgcid=author

(2) Please cite this paper and the original source of the dataset when using the code for academic purposes.

GB/T 7714: Fan L, Chen X, Chai Y, et al. Attribute fusion transfer for zero-shot fault diagnosis[J]. Advanced Engineering Informatics, 2023, 58: 102204.

BibTex:

@article{fan2023attribute, title={Attribute fusion transfer for zero-shot fault diagnosis}, author={Fan, Linchuan and Chen, Xiaolong and Chai, Yi and Lin, Wenyi}, journal={Advanced Engineering Informatics}, volume={58}, pages={102204}, year={2023}, publisher={Elsevier} }

Relationship between Code and Paper

(1) TABLE 6 AFT(our)

:code\AFT

(2) TABLE 4 NB-M

:code\NB-M

(3) TABLE 6 Transfer-TF

:code\Transfer-TF

Environment and Acknowledgement:

(1) Environment:

tensorflow-gpu 1.15.0

keras 2.2.4

scipy 1.5.2

pandas 1.0.5

numpy 1.19.1

(2) Acknowledgement: Thanks for the following references sincerely.

github：J.J. Downs, E.F. Vogel, A plant-wide industrial process control problem, Comput.Chem. Eng. 17 (3) (1993) 245–255.

github：https://github.com/foryichuanqi/RESS-Paper-2022.09-Remaining-useful-life-prediction-by-TaFCN