J.A. Delgado-Aguiñaga

Bio: J.A. Delgado-Aguiñaga is an academic researcher from Universidad del Valle de México. The author has contributed to research in topics: Leak & Extended Kalman filter. The author has an hindex of 5, co-authored 12 publications receiving 127 citations. Previous affiliations of J.A. Delgado-Aguiñaga include University of Grenoble & CINVESTAV.

Water Leak Diagnosis in Pressurized Pipelines: A Real Case Study

Abstract: This chapter presents a successful leak diagnosis for a real pipeline. The diagnosis was performed on the water pipeline West 5 located in Guadalajara City, Mexico, which is supervised by Inter-Municipal System of Potable Water and Sewage (SIAPA). Herein, the authors try to highlight those difficulties that arise when facing a real leak problem, especially if the fluid line is not monitored online or records of the flow rate and pressure are not available. By considering that only one leak is present in the system and by using the pipeline configuration information provided by the SIAPA staff, a discrete-time extended Kalman filter, used as a state observer, was designed in order to isolate the leak. The final decision about the leak location was based on the results of four different database analyses in which three showed a similar tendency.

Leak isolation based on Extended Kalman Filter in a plastic pipeline under temperature variations with real-data validation

Abstract: The present work is motivated by the purpose of considering a more realistic scenario than in former studies on the problem of leak isolation within a plastic pipeline, when the water can be affected by temperature changes. In order to address this situation, a state observer approach based on a model including temperature effect and an Extended Kalman Filter is proposed. Noting indeed that temperature affects some equivalent straight length of the pipe, which is used in the model, the observer estimates it together with the leak coefficients. This approach only considers head pressure and flow rate measurements coming from pipeline ends, likewise, water temperature measurement at upstream tank. Results with real data obtained from a pipeline prototype are shown in two different ways in order to illustrate the performance of proposed leak isolation system, as compared to the traditional approaches found at literature.

Computational Fluid Mechanics And Heat Transfer

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Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Abstract: Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.

Leak Detection and Location of Pipelines Based on LMD and Least Squares Twin Support Vector Machine

Abstract: In oil pipeline leak detection and location, noise in the pressure signal collected at the end of the pipeline affects the accuracy of leak detection and the error of leakage location. To reduce the noise interference, an improved local mean decomposition signal analysis method is proposed. The production functions (PFs) that are related to the leak signal can be exacted, and it is necessary to know the characteristics of leak signals or noise in advance. According to the cross-correlation function, there is a significant peak between the measured signals, which are decomposed into a number of PFs. These reconstructed principal PF components are obtained, and a wavelet analysis is used to remove the noise in the reconstructed signal. On this basis, the signal features are extracted according to the time-domain feature and the waveform feature, which are input into the least squares twin support vector machine (LSTSVM), to recognize pipeline leaks. According to the reconstructed signal after wavelet denoising, the time-delay estimate of the negative pressure signal at the end of the pipeline is obtained by the cross-correlation function, and the leak location is ultimately calculated by combining the time delay with the leak signal propagation velocity. A flow model for pipeline leakage is proposed based on the Flowmaster software, where the collected data of the different working conditions are processed. The experimental results show that the proposed method can effectively identify different working conditions and accurately locate the leakage point.