An unsupervised leak detection method with aggregating prediction and reconstruction along projection pathway for natural gas gathering pipelines

Abstract

Early detection of leaks in natural gas gathering pipelines makes proactive maintenance and corrective measures take place more effective, thus enhancing the safe and reliable operation of pipelines and mitigating serious threats to the environment and human life. The data-driven leak detection methods have become a natural choice owing to the extensive deployment of sensors in the pipeline network. However, the supervised and semi-supervised leak detection methods need to utilize a certain amount of leak data to train reliable classifiers, and labeled data usually difficult to obtain in real-world applications especially for the gas industry. Besides, most of data-driven methods today only consider the long-term or short-term patterns hidden in the multivariate time series rather than both, which may reduce their effectiveness since temporal data generated in the real-world applications often relates to a mixture of these two patterns. To settle this challenge, we propose a novel unsupervised leak detection method by utilizing a one-dimensional convolutional autoencoder (1D-CAE) to learn short-term dependency patterns from local time series and a long short-term memory (LSTM) to discover evolving trends about long-term patterns from entire temporal data. The proposed method not only calculates the difference between actual samples and their predictions but also compares the inputs and their reconstructions, where comparisons of reconstruction are further extended from input space to hidden spaces. The obtained deviations are then integrated through the developed integration strategy and employed to calculate their global leak scores. Specially, considering that the magnitudes of deviations integrated together are diverse or there may exist correlation among these deviations caused by correlated neurons across layers, the minimum covariance determinant (MCD) method is employed to scale deviations and eliminate correlations along the projection pathway. The effectiveness of the proposed method is verified on real datasets of natural gas gathering pipeline. In addition, analysis of experimental results demonstrates that the proposed method has good accuracy, and is conducive to operating in the context of uncontrolled long-term monitoring.