An early warning method of pipeline leakage monitoring with limited leakage samples

•A real-time transient simplified model for pipeline leak detection has been developed.•An optimal threshold interval selection method based on POD/POFC-threshold curve.•Improved efficiency of real-time transient modelling calculations.•Enhanced training of data-driven models with limited leakage samples. Aiming at the problems of low computational efficiency of the real-time transient model (RTTM), and training difficulty of data-driven models with small leakage samples in existing pipeline leakage monitoring methods, this paper proposes a novel pipeline leakage detection model (Multiple Regression Modeling of Real-time Transients, MR-RTTM) based on the fusion of real-time transient mechanism model and data-driven inference. Firstly, the real-time transient mechanism model of pipelines is derived based on the Bernoulli equation, and the required eigenvalues and label values of the data-driven model are determined to achieve the integration of the mechanism and the data-driven model. Secondly, MR-RTTM is constructed by applying the data of pipelines in normal working conditions and combining with the multiple regression algorithm to fit the real-time transient mechanism model of pipelines. An optimal threshold interval selection method of the model is also proposed based on the probability of detection (POD) and the probability of false call (POFC). Experiments show that MR-RTTM can detect pipeline leakage by arranging common pressure and flow rate sensors in the pipeline inlets and outlets, and the minimum detectable leakage is 1 % with an acceptable interval of 80 %∼100 % for POD and 0 ∼ 10 % for POFC, and the leakage discrimination rate is up to 100 %.