SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes

This repository is the official implementation of methods from the paper SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes.

Requirements

To install dependencies, run

pip install -r requirements.txt

Running experiments

Running experiments involves training and evaluating processes defined for each model in models folder. To run experiments, use the command line interface python main.py with the argument --config-name {model}_{dataset}. For example:

python main.py --config-name pca_kmeans_rieth_tep

Pretrained models are stored in saved_models folder. To evaluate a pretrained model, use path_to_model argument:

python main.py --config-name pca_kmeans_rieth_tep path_to_model=saved_models/pca_kmeans_rieth_tep.joblib

Experimental results are stored in results folder. All arguments are defined in configs folder.

Experimental results

Results on rieth_tep

	ACC	ARI	NMI	Detection TPR	Detection FPR	CDR	ADD
pca_kmeans	0.2745	0.1100	0.3634	0.3590	0.0000	0.7910	113.95
st_catgan	0.1754	0.1135	0.2223	0.3044	0.0000	0.3238	102.63
convae	0.1794	0.1565	0.2537	0.3631	0.0008	0.3664	164.76
sensorscan	0.5926	0.4747	0.6812	0.7316	0.0014	0.7351	57.15

Results on reinartz_tep

	ACC	ARI	NMI	Detection TPR	Detection FPR	CDR	ADD
pca_kmeans	0.3513	0.1316	0.4484	0.3581	0.0000	0.9562	113.33
st_catgan	0.3016	0.1287	0.3606	0.3627	0.0001	0.8882	135.04
convae	0.4975	0.2381	0.5863	0.6023	0.0016	0.9402	155.16
sensorscan	0.5287	0.3336	0.7551	0.9013	0.0002	0.7219	30.98

Citation

Please cite our paper as follows:

@article{golyadkin2023sensorscan,
  title={SensorSCAN: Self-supervised learning and deep clustering for fault diagnosis in chemical processes},
  author={Golyadkin, Maksim and Pozdnyakov, Vitaliy and Zhukov, Leonid and Makarov, Ilya},
  journal={Artificial Intelligence},
  volume={324},
  pages={104012},
  year={2023},
  publisher={Elsevier}
}