DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems

Our paper has been officially accepted for publication in the IEEE Internet of Things Journal, and is now available online. You can access it via the following DOI link: DOI: 10.1109/JIOT.2024.3381002.

Data

The synthetic dataset is included in the run/datasets/toy folder.
To prepare the Pronto dataset, download the raw data from PRONTO heterogeneous benchmark dataset and put it in the run/datasets/pronto/raw folder. Then, run the execute run/datasets/pronto/train_test_split.ipynb to prepare the dataset.

Code Structure

The code is organized in the following way:

.vscode/ contains the configuration files for debugging in visual studio code
src/model/ contains the implementation of the models
src/utils/ contains the implementation of the utility functions
src/train/ contains the implementation of the training functions
run/data/ contains the datasets used in the experiments
run/configs/ contains the configuration files used to run the experiments
run/main.py is the main file used to run the experiments
run/evaluate.py is the main file used to evaluate the models

Environment Setup

This project relies on specific dependencies and packages, which are defined in the eff_env.yml file. You can set up the environment using Conda by running the following command:

conda env create -f env.yml

If you want to update the environment, you can run the following command:

conda env update --file env.yml  --prune

Installing torch with GPU support

To install PyTorch with CUDA support, use the following command:

conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=10.2 -c pytorch

Depending on your CUDA version, you may need to change the cudatoolkit version. Detailed instructions can be found here. Note pyTorch 1.12.* binaries do not support CUDA versio above (including) 11.7.

Installing pytorch-geometric

Follow PyG 2.2.0 INSTALLATION Guide for detailed .

Usage

To train the model, run the following command:

python run/main.py --cfg run/configs/toy/dyedgegat.yaml --repeat 5

For any questions or feedback, please open an issue in this repository or contact us directly via email.

The --cfg argument specifies the path to the config file, and the --repeat argument specifies the number of times to repeat the experiment.

For evaluation, run the following command:

python run/evaluate.py --cfg run/configs/toy/dyedgegat.yaml