Enhanced Leak Detection and Localization in Liquid Pipelines Using an Improved Extended Kalman Filter

Abstract

To address the challenges of accuracy and stability in current detection methods, this paper proposes an enhanced online leak detection and localization approach based on an improved pipeline flow model and the Extended Kalman Filter within the state-space method. By establishing a comprehensive pipeline flow model, the proposed method relies solely on flow and pressure data at both ends of the pipeline, while also accounting for noise interference from instrumentation. This allows for precise leak location identification and calculation of key parameters such as leak volume, pressure, and leakage coefficient. The study primarily focuses on the linearization of noise variation and the refinement of the leakage detection model. The proposed method incorporates noise dynamics throughout the entire nonlinear calculation process and utilizes the Jacobian matrix to achieve noise linearization. To improve the leakage model, the leakage position is replaced with pipeline length, and the fourth-order Runge–Kutta method is used to solve the resulting equations. Data simulations and experimental validation confirm the effectiveness and accuracy of the proposed method. The experimental results indicate that the relative error in leak localization is less than 2%, with an accuracy improvement of 1.4% and 1.5% compared to conventional methods.