Leak Detection in Pipeline System: A Comparative Study

Dian Mursyitah; David Delouche; Frédéric Kratz

doi:10.22146/jnteti.v15i3.21441

Dian Mursyitah Department of Electrical Engineering, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Riau 28294, Indonesia
David Delouche JUNIA HEI Campus de Chateauroux, Chateauroux 36044, France
Frédéric Kratz PRISME Laboratory, INSA Centre Val de Loire, Bourges 18000, France

DOI: https://doi.org/10.22146/jnteti.v15i3.21441

Keywords: Leak Detection, Pipeline Systems, High Gain Observer (HGO), Extended Kalman Filter (EKF), Finite Memory Observer (FMO)

Abstract

Leak detection in pipeline systems is a critical challenge for ensuring safety, operational efficiency, and environmental sustainability. Conventional methods often struggle when applied to nonlinear systems subject to noise and uncertainty. This study aimed to perform a comparative analysis of four nonlinear observers, the high-gain observer (HGO), the extended Kalman filter (EKF), the EKF-based nonlinear observer (EKF-NO), and the finite memory observer (FMO), applied to a nonlinear pipeline model under leak scenarios. Each observer was evaluated via simulation for convergence speed, robustness to measurement noise, and sensitivity to initial conditions. Performance was assessed through simulation based on convergence speed, robustness to noise, and sensitivity to initial estimation errors. The results showed that the HGO achieved very fast convergence but amplified measurement noise significantly, thereby reducing detection sensitivity. The classical EKF produced smoother estimates but depended heavily on accurate model initialization and was sensitive to modeling errors. The EKF-NO improved estimation accuracy by accounting for nonlinear dynamics but converges more slowly than the FMO. Quantitative results showed that the FMO detected leaks in approximately 2 s, whereas the EKF required approximately 4–5 s to produce a reliable residual. In conclusion, the FMO achieved the best trade-off between convergence, robustness, and accuracy, producing the most stable and interpretable residuals for leak detection. Its robustness to noise and uncertain initial conditions makes it highly suitable for practical deployment in water, oil, and gas pipeline systems, and the approach shows strong potential for integration into real-time monitoring frameworks.

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