Pipeline leak and volume rate detections through Artificial intelligence and vibration analysis

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Pipeline monitoring provides operators with invaluable information regarding the potential risks that may pose threats to the integrity of the entire line. Pipeline leakage results in serious environmental and financial costs that can be avoided through leak detection systems. This study introduces a comprehensive leak monitoring system that allows leak detection, localization, and volume rate estimation in liquid pipelines installed above ground, simultaneously. To minimize the leak interpretation errors an artificial intelligence (AI)-based leak detection algorithm is developed. Pressure sensors are utilized to capture real-time variations of fluid pressure to localize pipeline leakage through the application of the pressure gradient intersection method. Vibrations of the pipeline are also acquired in real-time through accelerometers, the signals of which are then used to estimate the leak forces through the inverse dynamics of the pipeline between the leak location and the location of the accelerometers. This is achieved by developing a leak-induced vibration (LIV) model that simulates the dynamics of the pipe through a finite element (FE) vibration model. The transfer function of the pipe assembly is then used to design a Kalman filter. The Kalman filter predicts the leak forces and is used to estimate the fluid release through correlation analysis of the leak forces and leak volume rate, experimentally. A lab-scale experimental setup is manufactured to verify the dynamic LIV model and to test the proposed methodology. The performance of the proposed methodology shows 97 %, 96 %, and 92 % of accuracy on average for leak detection, localization, and leak volume rate estimation, respectively.