[4B3] Enhancing gas pipeline monitoring with graph neural networks: a new approach for acoustic emission analysis under variable pressure conditions
M Junayed Hasan¹, M Arifeen¹, M Sohaib², A Rohan¹ and S Kannan¹
¹Robert Gordon University, UK
²Zhejiang Normal University, China
Traditional machine learning (ML) and deep learning (DL)-based acoustic emission (AE) data-driven condition monitoring models face several reliability issues due to factors such as fluid pressure changes, flange vibrations, inconsistent leak lengths and noise in AE signals, which vary with pipeline conditions. Additionally, the noise and variable pressure conditions complicate the interpretation of sensor data, especially in multivariate set-ups where understanding spatial relationships between sensors is challenging. In response, we have introduced graph convolutional networks (GCNs) to overcome these challenges in AE-based pipeline monitoring for the first time. Our proposed method utilises a publicly available pipeline monitoring dataset, named GPLA-12, which comprises AE signals to train and evaluate the GCN-based model. This innovative graph construction technique is designed to decipher and comprehend the subtleties in AE signals gathered under various pressure conditions from a multivariate sensor set-up. This approach can potentially establish a new standard in pipeline monitoring research and applications.
