An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques

Nam, KiJeon; Ifaei, Pouya; Heo, SungKu; Rhee, Gahee; Lee, Seung−Chul

doi:10.3390/su11102970

Cited by 13 publications

(8 citation statements)

References 32 publications

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“…One day of periodicity is commonly adopted in statistical-based leakage detection methods (e.g. Ahn & Jung 2019;Nam et al 2019;Wu et al 2020). This is based on the assumption that the data at the same time every day follow the same distribution.…”

Section: Data Pre-processingmentioning

confidence: 99%

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Online leakage detection system based on EWMA-enhanced Tukey method for water distribution systems

Wan

Farmani

Keedwell

2022

Journal of Hydroinformatics

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Real-time leakage detection based on pressure and flow data has become increasingly essential for water distribution systems (WDSs). Recent data-driven leakage detection approaches have largely focused on burst detection characterised as sudden outflow or sudden pressure drops but did not mention the ability to detect gradual leakage events that do not have sudden change and could cause more water loss. This study proposes an online leakage detection system based on the exponential weighted moving average (EWMA) – enhanced Tukey method to help monitor gradual leakage events of WDSs. The proposed online system comprises three main parts: data pre-processing, the online detection sub-system, and the parameter updating sub-system. The proposed online system is based on lightweight and powerful statistical tools without complex model construction. The effectiveness of the proposed system is demonstrated on leakage datasets under various real-world scenarios, including gradual leakages and bursts. The results showed that the proposed EWMA-enhanced Tukey method could detect gradual leakage events quickly while generating low false alarms. The proposed method is computationally effective and able to deal with non-stationary behaviours automatically.

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Section: Data Pre-processingmentioning

confidence: 99%

“…EWMA exponentially weighted the average of all prior data so that it could utilise all the available information based on their importance to sense small changes in the process mean. Therefore, EWMA is an advanced method to monitor small or moderate shifts in process mean (Nam et al 2019).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Online leakage detection system based on EWMA-enhanced Tukey method for water distribution systems

Wan

Farmani

Keedwell

2022

Journal of Hydroinformatics

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“…The latter indicates the proposed algorithm in each method and the used district metering area (DMA). As illustrated in this table, the studied methodologies can be classified into different categories, namely prediction, 70,71 A comparison between the applied methodologies using myriad case studies should be provided. Accordingly, their results' discussion will be performed, one by one.…”

Section: Comparison With the Existing Methodsmentioning

confidence: 99%

“…For instance, Karray et al 70 study is unreliable as its methodology was tested in a small testbed. In a similar line, Nam et al 71 -Used Equipment may not be suitable to be employed in the real application.…”

Section: Comparison With the Existing Methodsmentioning

confidence: 99%

A genetic algorithm‐based intelligent solution for water pipeline monitoring system in a transient state

Abdelhafidh

Fourati

2020

Concurrency and Computation

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Water pipeline monitoring system becomes a relevant solution to cope with various pipeline hydraulic failures in order to save the environment from water losses. In this respect, cognitive water distribution system (WDS) combines Internet of Things (IoT) technology with Big Data generated by various connected objects and devices for reliable structural health monitoring of pipelines. Accordingly, designing a scalable WDS with smart leak detection and localization requires a serious study and an adequate planning. In this paper, we suggest a cognitive IoT-based architecture with adequate data collection based on the Apache Spark framework. Furthermore, our objective is to elaborate a hybrid mechanism that defines and performs accurate leakage identification and localization by taking into account both the steady-state and transient behavior of water. The bio-inspired sensitivity analysis of the water pressure profile based on the genetic algorithm ensures the effectiveness and the accuracy of the proposed monitoring solution.

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“…To predict failures, we need to build a failure prediction model that determines whether or not a failure occurs [15][16][17]. In order to build such a failure prediction model, most of the existing studies have used machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

Kwon

Kim

2020

Symmetry

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This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relevancy between each feature (i.e., each sensor) and the failure was analyzed using the random forest algorithm, to obtain the importance of the features. Then, feature selection and model building were conducted iteratively. In each iteration, a new feature was selected considering the importance and added to the selected feature set. The failure prediction model was built for each iteration via the support vector machine (SVM). Finally, the failure prediction model having the highest prediction accuracy was selected. The experimental implementation was conducted using open-source R. The results showed that the proposed failure prediction model achieved high prediction accuracy.

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An Efficient Burst Detection and Isolation Monitoring System for Water Distribution Networks Using Multivariate Statistical Techniques

Cited by 13 publications

References 32 publications

Online leakage detection system based on EWMA-enhanced Tukey method for water distribution systems

Online leakage detection system based on EWMA-enhanced Tukey method for water distribution systems

A genetic algorithm‐based intelligent solution for water pipeline monitoring system in a transient state

Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

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