Leak detection for transport pipelines based on autoregressive modeling

Wang, Guiquan; Dong, De-Ping; Fang, Chuan

doi:10.1109/19.206686

Cited by 35 publications

(19 citation statements)

References 3 publications

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“…In Wang et al (1993), a method of leak detection based on autoregressive modelling was proposed. This method requires four pressure measurements, two at each end of the pipeline.…”

Section: Discussionmentioning

confidence: 99%

Modeling of Hydraulic Load of Electric Drive in Electrical Complex of Pumping Station

Lysiak¹,

Oliinyk²,

Shelekh³

2018

Energy eng. control syst.

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Sudden pipe bursts occur in high-pressure water transmission pipelines and water distribution networks. The consequences of these bursts can be very expensive due to the outage time while the burst pipe is repaired, the cost of repair, and damage to surrounding property and infrastructure. As a result, it is advantageous to minimise the detection and location time after the burst occurs.Currently, there is no effective solution for the burst detection and location problem in water distribution systems. The applications in oil and gas pipelines and pipe networks show the advantages of continuous monitoring. A number of techniques are used to determine the location and size of a burst. One of the most promising approaches is fluid transient modelling and analysis. Pressure transient analysis has also been successfully applied for detection and location of existing leaks.A sudden pipe burst creates a negative pressure wave that travels in both directions away from the burst point. The analysis of this wave is the main principle for the techniques presented in this thesis. Experiences from previous research suggest that, to achieve the best performance, single pipelines and pipe networks have to be treated separately. Thus, two different approaches for burst detection and location are presented.When a burst occurs in a pipeline, the burst-induced pressure wave travels in both directions along the pipeline and is reflected at the boundaries. Using a pressure trace measured at one location along the pipeline, the timing of the initial and reflected burst-induced waves determines the location of the burst. The presented continuous monitoring technique uses the modified two-sided cumulative sum (CUSUM) algorithm to detect abrupt changes in the pressure iii data caused by the pipe break. The results from both laboratory and field pipelines are used to verify the proposed method. Different burst and measurement locations are tested. The results are promising for burst detection and location in real systems.In the network case, continuous pressure measurements at two locations are analysed. The burst detection and location algorithm is based on the difference between the arrival times of the burst-induced pressure wave at each measurement point and on the measured wave magnitude. The arrival times are determined automatically in real time. A method for determining optimal measurement locations is also presented. Results from numerical simulations show that the proposed technique has potential as a tool for effective detection and location of bursts in real pipe networks.

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“…In Wang et al (1993), a method of leak detection based on autoregressive modelling was proposed. This method requires four pressure measurements, two at each end of the pipeline.…”

Section: Discussionmentioning

confidence: 99%

Modeling of Hydraulic Load of Electric Drive in Electrical Complex of Pumping Station

Lysiak¹,

Oliinyk²,

Shelekh³

2018

Energy eng. control syst.

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“…The pressure across the valve is periodically measured by two sensors or meters at each side of the valve. In practice, a correlation usually exists between two successive pressure measurements at small sampling intervals (Wang et al 25 ). Therefore, we applied a first-order autoregressive (AR) model to the pipeline pressure series.…”

Section: Leakage Detection Across a Valvementioning

confidence: 99%

Cuscore control charts for generalized feedback‐control systems

Nembhard

Chen

2006

Quality & Reliability Eng

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The cumulative score (Cuscore) statistic is devised to 'resonate' with deviations or signals of an expected type. When a process signal subject to feedback control occurs, it results in a fault signature in the output error. In this paper, Cuscore statistics are designed to monitor process parameters and characteristics measured by a generalized minimum variance (GMV) feedback-control system sensitive to the fault signature of a spike, step, and bump signal. In this study, the GMV considered is a first-order dynamic system with autoregressive moving average (ARMA) noise. We show theoretically that the performance of Cuscore charts is independent of the amount of variability transferred from the output quality characteristic to the adjustment actions in the GMV control system. Simulation is used to test the performance using the Cuscore charts. In general, the Cuscore can detect signals over a broad range of system parameter values. However, areas of low detection capability occur for certain fault signatures. In these cases, a tracking signal test is combined with the Cuscore statistics to improve detection performance. This study provides several illustrations of the underlying behavior and shows how the methodology developed can be easily applied in practice.

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“…• Statistical data analysis-based methods: An autoregressive model, which uses two consecutive time sequences of pressure gradients at both ends of the pipeline, was established to detect the leak [75]. The parameters and residual variance of the fitted models are dependent on the condition of the pipeline and reflect the presence of a leak.…”

Section: ) Computing For Leak Monitoringmentioning

confidence: 99%

Computational Intelligence for Urban Infrastructure Condition Assessment: Water Transmission and Distribution Systems

Liu

Kleiner

2014

IEEE Sensors J.

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Leak detection for transport pipelines based on autoregressive modeling

Cited by 35 publications

References 3 publications

Modeling of Hydraulic Load of Electric Drive in Electrical Complex of Pumping Station

Modeling of Hydraulic Load of Electric Drive in Electrical Complex of Pumping Station

Cuscore control charts for generalized feedback‐control systems

Computational Intelligence for Urban Infrastructure Condition Assessment: Water Transmission and Distribution Systems

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