Early Prediction of Natural Gas Pipeline Leaks Using the MKTCN Model

TL;DR

The MKTCN model is proposed, which incorporates the Kolmogorov-Arnold Network as the fully connected layer in a dilated convolution model, enhancing network generalization and providing robust generalization and improved modeling of the long-term dependencies inherent in multi-dimensional time-series data.

Abstract

Natural gas pipeline leaks pose severe risks, leading to substantial economic losses and potential hazards to human safety. In this study, we develop an accurate model for the early prediction of pipeline leaks. To the best of our knowledge, unlike previous anomaly detection, this is the first application to use internal pipeline data for early prediction of leaks. The modeling process addresses two main challenges: long-term dependencies and sample imbalance. First, we introduce a dilated convolution-based prediction model to capture long-term dependencies, as dilated convolution expands the model's receptive field without added computational cost. Second, to mitigate sample imbalance, we propose the MKTCN model, which incorporates the Kolmogorov-Arnold Network as the fully connected layer in a dilated convolution model, enhancing network generalization. Finally, we validate the MKTCN model through extensive experiments on two real-world datasets. Results demonstrate that MKTCN outperforms in generalization and classification, particularly under severe data imbalance, and effectively predicts leaks up to 5000 seconds in advance. Overall, the MKTCN model represents a significant advancement in early pipeline leak prediction, providing robust generalization and improved modeling of the long-term dependencies inherent in multi-dimensional time-series data.

Figures (6)

• Figure 1: The overall architecture of the MKTCN model.
• Figure 2: Data processing method. Using this method, we transformer time-series data into serial data, improving generalization and removing the long-term dependence on the data.
• Figure 3: A box-and-line plot is employed to describe the original data distribution. At the same time, a heat map is utilized to illustrate the contribution of each feature in the original data, which PCA has processed.
• Figure 4: Precision-Recall curves in the NGPOD dataset. (a) Normal class. (b) Abnormal class. (c) Doubtful class.
• Figure 5: Confusion matrix for the MKTCN model on NGPOD dataset. Proportion is expressed as a percentage of the total. The correct statistic is indicated by green, while the incorrect statistic is indicated by red.