Fault Diagnosis of Process Systems Based on Graph Neural Network

Wentao Ouyang, Yang Jin

2024

Abstract

The fault diagnosis methods for process systems are generally based on rules and experience, which struggle with complex and uncertain issues. Therefore, In this study, a fault diagnosis method for process systems using adaptive Graph Neural Networks (GNNs) is proposed. This method effectively utilizes the correlations and dynamic changing among sensors, constructing a graph structure that reflects the complex relationships between sensors. By employing the graph convolutional neural network as the model foundation, it effectively extracts the primary changing features of faults, thereby addressing the problem of multi- class fault diagnosis. Comparative experiments were conducted using the fault diagnosis task of a three-phase flow system. The proposed method outperforms traditional models in terms of accuracy, precision, recall, and F1 score, demonstrating its effectiveness in fault diagnosis of process industrial systems.

Paper Citation

in Harvard Style

Ouyang W. and Jin Y. (2024). Fault Diagnosis of Process Systems Based on Graph Neural Network. In Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS; ISBN 978-989-758-715-3, SciTePress, pages 37-45. DOI: 10.5220/0012876300004536

in Bibtex Style

@conference{dmeis24,
author={Wentao Ouyang and Yang Jin},
title={Fault Diagnosis of Process Systems Based on Graph Neural Network},
booktitle={Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS},
year={2024},
pages={37-45},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012876300004536},
isbn={978-989-758-715-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS
TI - Fault Diagnosis of Process Systems Based on Graph Neural Network
SN - 978-989-758-715-3
AU - Ouyang W.
AU - Jin Y.
PY - 2024
SP - 37
EP - 45
DO - 10.5220/0012876300004536
PB - SciTePress