Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data

Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data

Zhang, Xinqi (China University of Petroleum (East China))
Shi, Jihao (China University of Petroleum (East China); The Hong Kong Polytechnic University)
Huang, Xinyan (The Hong Kong Polytechnic University)
Xiao, Fu (The Hong Kong Polytechnic University)
Yang, M. (TU Delft Safety and Security Science)
Huang, Jiawei (China University of Petroleum (East China))
Yin, Xiaokang (The Hong Kong Polytechnic University)
Sohail Usmani, Asif (The Hong Kong Polytechnic University)
Chen, Guoming (China University of Petroleum (East China))

2023

Deep learning has been widely applied to automated leakage detection and location of natural gas pipe networks. Prevalent deep learning approaches do not consider the spatial dependency of sensors, which limits leakage detection performance. Graph deep learning is a promising alternative to prevailing approaches as it can model spatial dependency. However, the challenge of collecting real-world anomaly data for training limits the accuracy and robustness of currently used graph deep learning approaches. This study proposes a deep probabilistic graph neural network in which attention-based graph neural network is built to model spatial sensor dependency. Variational Bayesian inference is integrated to model the posterior distribution of sensor dependency so that the leakage can be localized. An urban natural gas pipe network experiment is employed to construct the benchmark dataset, in which normal time-series data is applied to develop our proposed model while anomaly leakage data is used for performance comparison between our model and other state-of-the-art models. The results demonstrate that our model exhibits competitive detection accuracy (AUC) = 0.9484, while the additional uncertainty interval provides more comprehensive leakage detection information compared to state-of-the-art deep learning models. In addition, our model's posterior distribution enhances the leakage localization with the accuracy of positioning (PAc) = 0.8, which is higher than that of other state-of-the-art graph deep learning models. This study provides a comprehensive and robust alternative for subsequent decision-making to mitigate natural gas leakage from pipe networks.

Digital twin
Graph deep learning
Leakage detection
Leakage localization
Variation Bayesian Inference

http://resolver.tudelft.nl/uuid:88161c48-f59a-481c-ba61-4ab5ef2d9474

https://doi.org/10.1016/j.eswa.2023.120542

2023-11-30

0957-4174

Expert Systems with Applications, 231

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Xinqi Zhang, Jihao Shi, Xinyan Huang, Fu Xiao, M. Yang, Jiawei Huang, Xiaokang Yin, Asif Sohail Usmani, Guoming Chen