Distance-Based Burst Detection Using Multiple Pressure Sensors in District Metering Areas

Wu, Yipeng; Liu, Shuming; Wang, Xiaoting

doi:10.1061/(asce)wr.1943-5452.0001001

Cited by 20 publications

(4 citation statements)

References 12 publications

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“…Abokifa et al [41] demonstrated that an optimal window period can be selected to achieve the best performance, while smaller and larger windows would generally yield less accurate results. Wu et al [42] drew the conclusion that detection performance of the method changes only slightly when STWL varies from 1 to 6 days.…”

Section: Resultsmentioning

confidence: 99%

Time-Series-Based Leakage Detection Using Multiple Pressure Sensors in Water Distribution Systems

Shao

Zhang

et al. 2019

Sensors

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Leak detection is nowadays an important task for water utilities as leakages in water distribution systems (WDS) increase economic costs significantly and create water resource shortages. Monitoring data such as pressure and flow rate of WDS fluctuate with time. Diagnosis based on time series monitoring data is thought to be more convincing than one-time point data. In this paper, a threshold selection method for the correlation coefficient based on time series data is proposed based on leak scenario falsification, to explore the advantages of data interpretation based on time series for leak detection. The approach utilizes temporal varying correlation between data from multiple pressure sensors, updates the threshold values over time, and scans multiple times for a scanning time window. The effect of scanning time window length on threshold selection is also tested. The performance of the proposed method is tested on a real, full-scale water distribution network using synthetic data, considering the uncertainty of demand and leak flow rates, sensor noise, and so forth. The case study shows that the scanning time window length of 3–6 achieves better performance; the potential of the method for leak detection performance improvement is confirmed, though affected by many factors such as modeling and measurement uncertainties.

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Section: Resultsmentioning

confidence: 99%

Time-Series-Based Leakage Detection Using Multiple Pressure Sensors in Water Distribution Systems

Shao

Zhang

et al. 2019

Sensors

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“…Daily time series have been analyzed to consider these time-series data [14][15][16]. Furthermore, in this study, we designed various time series patterns to extract the characteristic points of minute-scale trends and periodicity [17]. Similarly, in this study, various sets of time-series data were established.…”

Section: Configuring a Leakage Detection Modelmentioning

confidence: 99%

Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM

Lee

Yoo

2021

Sustainability

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With the advent of the 4th Industrial Revolution, advanced measurement infrastructure and utilization technologies are being noticeably introduced into the water supply system to store and utilize measurement data. From this perspective, the leak detection technology in water supply networks is becoming increasingly vital to sustainable water resource management and the clean water supply worldwide. In particular, leakage detection of buried pipelines is rated as a very challenging research topic given the current level of technology. However, leakage in buried underground pipelines is rated as a very challenging research topic given the current level of technology. Therefore, a data-driven leak detection model was developed through this study using deep learning technology based on inflow meter data. Multiple threshold-based models were applied to reduce the RNN-LSTM (Recurrent Neural Networks–Long Short-Term Memory models) deep learning and false prediction range, which is programmed in conjunction with the Python language and Google Colaboratory (a big data analysis tool). The developed model consists of flow pattern shape extraction, RNN-LSTM-based flow prediction, and threshold setting modules. The developed model was applied to the actual leakage accident data, followed by the performance evaluation. As a result, the leak was recognized at most points immediately after the accident. The performance of leak detection was evaluated by a Confusion matrix and showed more than 90% accuracy at all points except singularities. Therefore, the developed model can be used as a critical software technology to proactively identify various at present with smart water infrastructure being introduced. In addition, this model is highly scalable as it can consider various operational situations based on the expert system, and it can also efficiently reflect the results of pipe network analysis across different scenarios.

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“…Other authors have proposed the use of statistical risk functions (Cheng et al 2018) and principal component analysis (Palau et al 2012) to detect bursts in transmission mains and in DMAs. A different approach was described in Wu et al (2018a), where a clustering-based method was used to identify bursts using only 1 day of historic measured data without using statistical methods to model the expected normal conditions of the system. Although these techniques are successful in detecting the occurrence of bursts, they are unable to accurately pinpoint the location of bursts and their range of effectiveness is often limited to a DMA level.…”

Section: Introductionmentioning

confidence: 99%

Merging Fluid Transient Waves and Artificial Neural Networks for Burst Detection and Identification in Pipelines

Bohórquez

Simpson

Lambert

et al. 2021

J. Water Resour. Plann. Manage.

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Distance-Based Burst Detection Using Multiple Pressure Sensors in District Metering Areas

Cited by 20 publications

References 12 publications

Time-Series-Based Leakage Detection Using Multiple Pressure Sensors in Water Distribution Systems

Time-Series-Based Leakage Detection Using Multiple Pressure Sensors in Water Distribution Systems

Development of Leakage Detection Model and Its Application for Water Distribution Networks Using RNN-LSTM

Merging Fluid Transient Waves and Artificial Neural Networks for Burst Detection and Identification in Pipelines

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