Wyniki wyszukiwania - Biblioteka Nauki

1

Fast leak detection and location of gas pipelines based on an adaptive particle filter

100%

Liu M. , Zang S. , Zhou D.

International Journal of Applied Mathematics and Computer Science

|

2005

|

tom 15

|

nr 4

541-550

EN

Leak detection and location play an important role in the management of a pipeline system. Some model-based methods, such as those based on the extended Kalman filter (EKF) or based on the strong tracking filter (STF), have been presented to solve this problem. But these methods need the nonlinear pipeline model to be linearized. Unfortunately, linearized transformations are only reliable if error propagation can be well approximated by a linear function, and this condition does not hold for a gas pipeline model. This will deteriorate the speed and accuracy of the detection and location. Particle filters are sequential Monte Carlo methods based on point mass (or ``particle'') representations of probability densities, which can be applied to estimate states in nonlinear and non-Gaussian systems without linearization. Parameter estimation methods are widely used in fault detection and diagnosis (FDD), and have been applied to pipeline leak detection and location. However, the standard particle filter algorithm is not applicable to time-varying parameter estimation. To solve this problem, artificial noise has to be added to the parameters, but its variance is difficult to determine. In this paper, we propose an adaptive particle filter algorithm, in which the variance of the artificial noise can be adjusted adaptively. This method is applied to leak detection and location of gas pipelines. Simulation results show that fast and accurate leak detection and location can be achieved using this improved particle filter.

2

Leakage detection from liquid transmission pipelines using improved pressure wave technique

75%

Ostapkowicz P.

Eksploatacja i Niezawodność

|

2014

|

tom Vol. 16, no. 1

9--16

PL

Artykuł dotyczy zagadnień diagnozowania wycieków z rurociągów przesyłowych cieczy. Skupia się na polepszaniu skuteczności metody opartej na detekcji fal ciśnienia. Zaproponowano nowy algorytm do monitorowania fal ciśnienia. Algorytm jest ukierunkowany na precyzyjną identyfikację charakterystycznych punktów na przebiegach sygnałów ciśnienia reprezentujących fale wywołane przez zaistniały wyciek. Działanie algorytmu jest oparte o filtrację medianową residuów wyznaczanych dla sygnałów ciśnienia mierzonych wzdłuż rurociągu. Zastosowano adaptacyjne progi alarmowe, obliczane na podstawie analizy statystycznej. Dodatkowo, algorytm wspomagany jest przez wykorzystanie zbioru funkcji korelacji wzajemnej pomiędzy obliczanymi residuami reprezentującymi sygnały ciśnienia z sąsiednich przetworników pomiarowych. Zaproponowane rozwiązanie zostało przetestowane na fizycznym modelu rurociągu, którym tłoczono wodę. Rurociąg ma 380 m długości, średnicę wewnętrzną 34 mm i został wykonany z rur z polietylenu (PEHD). Wyniki badań udowodniły, że proponowane rozwiązanie jest wrażliwe na małe wycieki i odporne na fałszywe alarmy (występujące zakłócenia). Pozwala na zadawalająco dokładną lokalizację wycieku, bez znaczących opóźnień czasowych.

EN

This paper deals with leak detection in liquid transmission pipelines. It focuses on improving the efficiency of a method based on negative pressure wave detection. A new algorithm for pressure wave monitoring has been proposed. The algorithm is aimed to precisely capture the corresponding characteristic points in the signal sequence of negative pressure waves caused by leakage. It uses median filtering of the calculating deviations of pressure signals measured along the pipeline. Adaptive alarm thresholds with reduced margins, based on statistical analysis of the calculating deviations of pressure signals, were used. Additionally, the algorithm is supported by a set of functions base on the calculation of the cross-correlation of the deviations which represent pressure signals from neighboring transducers. The developed technique has been tested on a physical model of pipeline. The pipeline is 380 meters long and 34 mm in internal diameter, and is made of polyethylene (PEHD) pipes. The medium pumped through the pipeline was water. Tests proved, that the proposed solution is sensitive to small leaks and resistant for false alarm (occurring disturbances). It is also capable of localizing the leak point with satisfactory accuracy, without significant delay.

3

Fast leak detection and location of gas pipelines based on an adaptive particle filter

75%

Liu M. , Zang S. , Zhou D.

International Journal of Applied Mathematics and Computer Science

|

2005

|

tom Vol. 15, no 4

541-550

EN

Leak detection and location play an important role in the management of a pipeline system. Some model-based methods, such as those based on the extended Kalman filter (EKF) or based on the strong tracking filter (STF), have been presented to solve this problem. But these methods need the nonlinear pipeline model to be linearized. Unfortunately, linearized transformations are only reliable if error propagation can be well approximated by a linear function, and this condition does not hold for a gas pipeline model. This will deteriorate the speed and accuracy of the detection and location. Particle filters are sequential Monte Carlo methods based on point mass (or “particle”) representations of probability densities, which can be applied to estimate states in nonlinear and non-Gaussian systems without linearization. Parameter estimation methods are widely used in fault detection and diagnosis (FDD), and have been applied to pipeline leak detection and location. However, the standard particle filter algorithm is not applicable to time-varying parameter estimation. To solve this problem, artificial noise has to be added to the parameters, but its variance is difficult to determine. In this paper, we propose an adaptive particle filter algorithm, in which the variance of the artificial noise can be adjusted adaptively. This method is applied to leak detection and location of gas pipelines. Simulation results show that fast and accurate leak detection and location can be achieved using this improved particle filter.