Search Results (3)

A This paper proposes a modified real-time transient modelling (MRTTM) framework to address the critical challenge of leak detection and localization in pipeline transmission systems. Pipelines are essential infrastructure for transporting liquids and gases, but they are susceptible to leaks, with severe environmental and economic impacts. MRTTM tackles this challenge with a three-stage operational process. First, “Data Collection” gathers sensor data from designated observation points. Second, the “Detection” stage identifies leaks. Finally, “Decision-Making” utilizes MRTTM to pinpoint the exact leak magnitude and location. This paper introduces an innovative method designed to significantly enhance pipeline leak detection and localization through the application of artificial intelligence and advanced signal processing techniques. The improved MRTTM framework integrates AI for pattern recognition, state space modelling for leak segment identification, and an extended Kalman filter (EKF) for precise leak location estimation, addressing the limitations of traditional methods. This paper showcases the application of MRTTM through a case study using the K-nearest neighbors (KNN) method on a water transmission pipeline for leak detection. KNN aids in classifying leak patterns and identifying the most likely leak location. Additionally, MRTTM incorporates the EKF, enabling real-time updates during transient events for faster leak identification. Preprocessing sensor data before comparison with the leakage pattern bank (LPB) minimizes false alarms and enhances detection reliability. Overall, the AI-powered MRTTM framework offers a powerful solution for swift and precise leak detection and localization in pipeline systems. The functionality of the framework is examined, and the results effectively approve the effectiveness of this methodology. The experimental results validate the practical utility of the MRTTM framework in real-world applications, demonstrating up to 90% detection accuracy and an F1 score of 0.92. Full article

Improving leak detection capability to eliminate undetected releases is an area of focus for the energy pipeline industry, and the pipeline companies are working to improve existing methods for monitoring their pipelines. Computer model-based leak detection methods that detect leaks by analyzing the pipeline hydraulic state have been widely employed in the industry, but their effectiveness in practical applications is often challenged by real-world uncertainties. This study quantitatively assessed the effects of uncertainties on leak detectability of a commonly used real-time transient model-based leak detection system. Uncertainties in fluid properties, field sensors, and the data acquisition system were evaluated. Errors were introduced into the input variables of the leak detection system individually and collectively, and the changes in leak detectability caused by the uncertainties were quantified using simulated leaks. This study provides valuable quantitative results contributing towards a better understanding of how real-world uncertainties affect leak detection. A general ranking of the importance of the uncertainty sources was obtained: from high to low it is time skew, bulk modulus error, viscosity error, and polling time. It was also shown that inertia-dominated pipeline systems were less sensitive to uncertainties compared to friction-dominated systems. Full article

Analysis of transient fluid pressure signals has been investigated as an alternative method of fault detection in pipeline systems and has shown promise in both laboratory and field trials. The advantage of the method is that it can potentially provide a fast and cost effective means of locating faults such as leaks, blockages and pipeline wall degradation within a pipeline while the system remains fully operational. The only requirement is that high speed pressure sensors are placed in contact with the fluid. Further development of the method requires detailed numerical models and enhanced understanding of transient flow within a pipeline where variations in pipeline condition and geometry occur. One such variation commonly encountered is the degradation or thinning of pipe walls, which can increase the susceptible of a pipeline to leak development. This paper aims to improve transient-based fault detection methods by investigating how changes in pipe wall thickness will affect the transient behaviour of a system; this is done through the analysis of laboratory experiments. The laboratory experiments are carried out on a stainless steel pipeline of constant outside diameter, into which a pipe section of variable wall thickness is inserted. In order to detect the location and severity of these changes in wall conditions within the laboratory system an inverse transient analysis procedure is employed which considers independent variations in wavespeed and diameter. Inverse transient analyses are carried out using a genetic algorithm optimisation routine to match the response from a one-dimensional method of characteristics transient model to the experimental time domain pressure responses. The accuracy of the detection technique is evaluated and benefits associated with various simplifying assumptions and simulation run times are investigated. It is found that for the case investigated, changes in the wavespeed and nominal diameter of the pipeline are both important to the accuracy of the inverse analysis procedure and can be used to differentiate the observed transient behaviour caused by changes in wall thickness from that caused by other known faults such as leaks. Further application of the method to real pipelines is discussed. Full article