A semi-supervised leakage detection method driven by multivariate time series for natural gas gathering pipeline

Abstract

Real-time and accurate leakage detection of natural gas gathering pipelines is critical to the safe and reliable operation of the gas and oil industry. Modern data-driven fault detection and diagnosis techniques have recently gained increasing attention because model-based techniques are practically prohibitive. However, most existing data-driven leakage detection approaches are obtained through supervised learning, which requires a substantial set of labelled data. Especially in real-world scenarios, leakage samples are rare. To reduce the dependence of the leakage detection method on leakage data and make full use of numerous normal datasets generated under normal working conditions, we propose a semi-supervised leakage detection method which consists of two components: an improved long short term memory autoencoder (LSTM-AE) network and one-class support vector machine (OCSVM). The LSTM-AE is first trained to learn the intrinsic features of a normal dataset of pipeline parameters which are multivariate time series. The OCSVM is then applied to calculate the score which is used to infer leak existence. The performance of the proposed method is evaluated on the real natural gas gathering pipelines, and the results confirm that the proposed method achieved 98% accuracy and 99% AUC (Area Under Curve) in a real-life dataset.