Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system

Mounce, S. R.; Khan, Asar; Wood, Alastair S.; Day, Andrew J.; Widdop, Peter D.; Machell, John

doi:10.1016/s1566-2535(03)00034-4

Cited by 82 publications

(31 citation statements)

References 18 publications

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“…Similarly, a sensor placement and leakage detection methodology to identify leakages in a WDS based on the deviation of sensor pressure from an estimated pressure was presented by Perez et al [29]. Some other research works employ the benefits of the artificial intelligence system for leakage detection purposes [30][31][32]. Mounce et al [30] proposed a leakage detection method based on an artificial neural network to harmonise data obtained from different sensors to classify different types of leakage in a WDS.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Some other research works employ the benefits of the artificial intelligence system for leakage detection purposes [30][31][32]. Mounce et al [30] proposed a leakage detection method based on an artificial neural network to harmonise data obtained from different sensors to classify different types of leakage in a WDS. The developed methodology is based on sensor time series data and thus requires a large monitoring database.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In many of these research works, the location of the pipe burst is identified by using the arrival times and magnitudes of burst-induced transient waves at two or more points where the pressure sensors were stationed. Background type leakages are not detected by measuring instruments and go unreported for a long period of time, thus, the techniques based on pressure sensor data [28][29][30][31]33] are not effective in detecting background type leakages.…”

Section: Background and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks

Adedeji

Hamam

Abe

et al. 2017

Water

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Water loss through leaking pipes constitutes a major challenge to the operational service of water utilities. In recent years, increasing concern about the financial loss and environmental pollution caused by leaking pipes has been driving the development of efficient algorithms for detecting leakage in water piping networks. Water distribution networks (WDNs) are disperse in nature with numerous number of nodes and branches. Consequently, identifying the segment(s) of the network and the exact leaking pipelines connected to this segment(s) where higher background leakage outflow occurs is a challenging task. Background leakage concerns the outflow from small cracks or deteriorated joints. In addition, because they are diffuse flow, they are not characterised by quick pressure drop and are not detectable by measuring instruments. Consequently, they go unreported for a long period of time posing a threat to water loss volume. Most of the existing research focuses on the detection and localisation of burst type leakages which are characterised by a sudden pressure drop. In this work, an algorithm for detecting and estimating background leakage in water distribution networks is presented. The algorithm integrates a leakage model into a classical WDN hydraulic model for solving the network leakage flows. The applicability of the developed algorithm is demonstrated on two different water networks. The results of the tested networks are discussed and the solutions obtained show the benefits of the proposed algorithm. A noteworthy evidence is that the algorithm permits the detection of critical segments or pipes of the network experiencing higher leakage outflow and indicates the probable pipes of the network where pressure control can be performed. However, the possible position of pressure control elements along such critical pipes will be addressed in future work.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks

Adedeji

Hamam

Abe

et al. 2017

Water

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“…The algorithms and software employed, as well as further background on system integration, are described in more detail in Mounce et al (2003) and Mounce et al (2010). Abnormal classifications by the FIS were entered into an alerts database, and automated email alerts were sent to the water company's control room staff.…”

Section: Automated Data Analysis For Burst Detectionmentioning

confidence: 99%

Online data processing for proactive UK water distribution network operation

Machell

Mounce

Farley

et al. 2014

Drink. Water Eng. Sci.

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Abstract. Operational benefits and efficiencies generated using prevalent water industry methods and techniques are becoming more difficult to achieve; as demonstrated by English and Welsh water companies' static position with regards the economic level of leakage. Water companies are often unaware of network incidents such as burst pipes or low pressure events until they are reported by customers; and therefore use reactive strategies to manage the effects of these events. It is apparent that new approaches need to be identified and applied to promote proactive network management if potential operational productivity and standards of service improvements are to be realised. This paper describes how measured flow and pressure data from instrumentation deployed in a UK water distribution network was automatically gathered, checked, analysed and presented using recently developed techniques to generate apposite information about network performance. The work demonstrated that these technologies can provide early warning, and hence additional time to that previously available, thereby creating opportunity to proactively manage a network; for example to minimise the negative impact on standards of customer service caused by unplanned events such as burst pipes.Each method, applied individually, demonstrated improvement on current industry processes. Combined application resulted in further improvements; including quicker and more localised burst main location. Future possibilities are explored, from which a vision of seamless integration between such technologies emerges to enable proactive management of distribution network events.

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“…이러한 문제점의 보완을 위하여 관로 상에 설치된 센서 로부터 얻어지는 유량 및 압력데이터 분석을 통한 자동감 시는 파열 및 누수 발생 유무에 대한 정보를 실시간으로 파악하려는 연구들이 수행되어 왔다 (Mounce et al, 2003;Khan et al, 2005;Ha et al, 2006). 자동 파열누수 감시에 대한 그동안의 연구는 인공신경망(artificial neural network) 기법을 이용하여 전형적인 용수수요 패턴의 학습 을 통하여 비정상정인 용수사용량 및 누수량을 예측한다.…”

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Leakage Detection of Water Distribution System using Adaptive Kalman Filter

Kim¹,

Choi²,

Bae³

et al. 2013

Journal of Korea Water Resources Association

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Leakage in water distribution system causes social and economic losses by direct water loss into the ground, and additional energy demand for water supply. This research suggests a leak detection model of using adaptive Kalman filtering on real-time data of pipe flow. The proposed model takes into account hourly and daily variations of water demand. In addition, the model's prediction accuracy is improved by automatically calibrating the covariance of noise through innovation sequence. The adaptive Kalman filtering shows more accurate result than the existing Kalman method for virtual sine flow data. Then, the model is applied to data from two real district metered area in JE city. It is expected that the proposed model can be an effective tool for operating water supply system through detecting burst leakage and abnormal water usage.

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Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system

Cited by 82 publications

References 18 publications

Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks

Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks

Online data processing for proactive UK water distribution network operation

Leakage Detection of Water Distribution System using Adaptive Kalman Filter

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