scholarly journals Data-driven and model-based framework for smart water grid anomaly detection and localization

2021 ◽  
Author(s):  
Z. Y. Wu ◽  
A. Chew ◽  
X. Meng ◽  
J. Cai ◽  
J. Pok ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Model Calibration ◽  
Software Tool ◽  
Hydraulic Modeling ◽  
Hydraulic Model ◽  
Data Driven ◽  
Calibration Data ◽  
Statistical Process ◽  
Anomaly Classification ◽  
Detection And Localization

Abstract With increasing adoption of advanced meter infrastructure, smart sensors together with SCADA systems, it is imperative to develop novel data analytics and couple the results with hydraulic modeling to improve the quality and efficiency of water services. One important task is to timely detect and localize anomaly events, which may include, but not be limited to, pipe bursts and unauthorized water usages. In this paper, a comprehensive solution framework has been developed for anomaly detection and localization by formulating and integrating data-driven analytics with hydraulic model calibration. Data analysis for anomaly detection proceeds in multiple steps including the following: (1) data pre-processing to eliminate and correct erroneous data records, (2) outlier detection by statistical process control methods and deep machine learning, and (3) system anomaly classification by correlation analysis of multiple sensor events. Classified system anomaly events are subsequently localized via hydraulic model calibration. The integrated solution framework is developed as a user-friendly and effective software tool, tested, and validated on the selected target areas in Singapore.

scholarly journals USE OF HYDRAULIC MODEL FOR WATER LOSS REDUCTION / HIDRAULINIO MODELIO TAIKYMAS VANDENS NUOSTOLIAMS MAŽINTI

2016 ◽  
Vol 8 (4) ◽  
pp. 461-467 ◽  
Author(s):  
Mindaugas Rimeika ◽  
Anželika Jurkienė
Keyword(s):  
Water Supply ◽  
Water Loss ◽  
Model Calibration ◽  
System Optimization ◽  
Hydraulic Modeling ◽  
Hydraulic Model ◽  
Supply System ◽  
Water Supply System ◽  
Loss Reduction ◽  
Program Selection

Hydraulic modeling is the modern way to apply world water engineering experience in every day practice. Hydraulic model is an effective tool in order to perform analysis of water supply system, optimization of its operation, assessment of system efficiency potential, evaluation of water network development, fire flow capabilities, energy saving opportunities and water loss reduction and ect. Hydraulic model shall include all possible engineering elements and devices allocated in a real water supply system regardless to hydraulic modeling program selection. In order to create high quality hydraulic model it is necessary to carry out measurement in water supply system and perform model calibration. The article presents principles and examples of formation of district metered areas and examples of model calibration. The article also presents case study of Šiauliai water supply system model application for water loss evaluation and localization. Determined that hydraulic model of water supply system helps to significantly reduce search areas of physical water loss formation and improves efficiency of further detection of water loss formation points by saving time and resources. Hidraulinis modeliavimas yra šiuolaikiškas ir modernus būdas, taikomas pasaulinėje vandentvarkos inžinerijos praktikoje. Hidraulinis modelis yra puikus darbo įrankis atliekant vandens tiekimo sistemos analizę, optimizuojant jos darbą, siekiant įvertinti sistemos darbo efektyvumo didinimo galimybes, vandentiekio tinklo plėtrą, gaisrų gesinimo galimybes, elektros energijos taupymo galimybes, vandens nuostolių paiešką ir kt. Nepriklausomai nuo hidraulinio modeliavimo programos pasirinkimo, hidraulinis modelis turi būti sudarytas iš daugelio inžinerinių elementų ir įrenginių, esančių realioje vandens tiekimo sistemoje. Kokybiškam hidrauliniam modeliui sukurti būtina atlikti slėgio ir debito matavimus bei modelio kalibravimą. Straipsnyje pateikiami debito ir slėgio matavimo vietų parinkimo principai bei modelio kalibravimo pavyzdžiai. Aprašyti Šiaulių miesto vandens tiekimo sistemos hidraulinio modelio taikymo pavyzdžiai vandens nuostolių lygiui nustatyti ir jų susidarymo vietoms lokalizuoti. Nustatyta, kad hidraulinis vandens tiekimo sistemos modelis padeda itin sumažinti fizinių vandens nuostolių paieškos plotą, o tolimesnis nuostolių vietos nustatymo įrangos taikymas gali būti žymiai efektyvesnis, taupant darbo laiką ir išteklius.

scholarly journals Water leakage detection and localization using hydraulic modeling and classification

2021 ◽  
Author(s):  
Eliyas Girma Mohammed ◽  
Ethiopia Bisrat Zeleke ◽  
Surafel Lemma Abebe
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Hybrid Approach ◽  
Leak Detection ◽  
Hydraulic Modeling ◽  
Hydraulic Model ◽  
Leakage Detection ◽  
Minimum Night Flow ◽  
Two Phases ◽  
Detection And Localization

Abstract A significant percentage of treated water is lost due to leakage in water distribution systems. The state-of-the-art leak detection and localization schemes use a hybrid approach of hydraulic modeling and data-driven techniques. Most of these works, however, focus on single leakage detection and localization. In this research, we propose to use combined pressure and flow residual data to detect and localize multiple leaks. The proposed approach has two phases: detection and localization. The detection phase uses the combination of pressure and flow residuals to build a hydraulic model and classification algorithm to identify leaks. The localization phase analyzes the pattern of isolated leak residuals to localize multiple leaks. To evaluate the performance of the proposed approach, we conducted experiments using Hanoi Water Network benchmark and a dataset produced based on LeakDB benchmark's dataset preparation procedure. The result for a well-calibrated hydraulic model shows that leak detection is 100% accurate while localization is 90% accurate, thereby outperforming minimum night flow and raw- and residual-based methods in localizing leaks. The proposed approach performed relatively well with the introduction of demand and noise uncertainty. The proposed localization approach is also able to locate two to four leaks that existed simultaneously.

A comparative study of data-driven and physics-based gas turbine fault recognition approaches

2021 ◽  
pp. 1748006X2198964
Author(s):  
Juan Luis Pérez-Ruiz ◽  
Igor Loboda ◽  
Iván González-Castillo ◽  
Víctor Manuel Pineda-Molina ◽  
Karen Anaid Rendón-Cortés ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Recognition Accuracy ◽  
Data Driven ◽  
Fault Identification ◽  
Fault Recognition ◽  
Data Driven Approach ◽  
Diagnostic Approaches ◽  
Artificial Neural Network Ann ◽  
Two Stages

The present paper compares the fault recognition capabilities of two gas turbine diagnostic approaches: data-driven and physics-based (a.k.a. gas path analysis, GPA). The comparison takes into consideration two differences between the approaches, the type of diagnostic space and diagnostic decision rule. To that end, two stages are proposed. In the first one, a data-driven approach with an artificial neural network (ANN) that recognizes faults in the space of measurement deviations is compared with a hybrid GPA approach that employs the same type of ANN to recognize faults in the space of estimated fault parameter. Different case studies for both anomaly detection and fault identification are proposed to evaluate the diagnostic spaces. They are formed by varying the classification, type of diagnostic analysis, and deviation noise scheme. In the second stage, the original GPA is reconstructed replacing the ANN with a tolerance-based rule to make diagnostic decisions. Here, two aspects are under analysis: the comparison of GPA classification rules and whole approaches. The results reveal that for simple classifications both spaces are equally accurate for anomaly detection and fault identification. However, for complex scenarios, the data-driven approach provides on average slightly better results for fault identification. The use of a hybrid GPA with ANN for a full classification instead of an original GPA with tolerance-based rule causes an increase of 12.49% in recognition accuracy for fault identification and up to 54.39% for anomaly detection. As for the whole approach comparison, the application of a data-driven approach instead of the original GPA can lead to an improvement of 12.14% and 53.26% in recognition accuracy for fault identification and anomaly detection, respectively.

A Data-Driven Heart Disease Prediction Model Through K-Means Clustering-Based Anomaly Detection

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Rony Chowdhury Ripan ◽  
Iqbal H. Sarker ◽  
Syed Md. Minhaz Hossain ◽  
Md. Musfique Anwar ◽  
Raza Nowrozy ◽  
...  
Keyword(s):  
Heart Disease ◽  
Anomaly Detection ◽  
Prediction Model ◽  
Data Driven ◽  
Disease Prediction

Solar farm voltage anomaly detection using high-resolution μPMU data-driven unsupervised machine learning

2021 ◽  
Vol 303 ◽  
pp. 117656
Author(s):  
Maitreyee Dey ◽  
Soumya Prakash Rana ◽  
Clarke V. Simmons ◽  
Sandra Dudley
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Anomaly Detection ◽  
Data Driven ◽  
Unsupervised Machine Learning

scholarly journals An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2813
Author(s):  
Muslikhin Muslikhin ◽  
Jenq-Ruey Horng ◽  
Szu-Yueh Yang ◽  
Ming-Shyan Wang ◽  
Baiti-Ahmad Awaluddin
Keyword(s):  
Artificial Intelligence ◽  
System Performance ◽  
Industry 4.0 ◽  
Online Shopping ◽  
Robotic Manipulators ◽  
Data Driven ◽  
Key Points ◽  
Detection And Localization ◽  
Picking System

In this study, an Artificial Intelligence of Things (AIoT)-based automated picking system was proposed for the development of an online shop and the services for automated shipping systems. Speed and convenience are two key points in Industry 4.0 and Society 5.0. In the context of online shopping, speed and convenience can be provided by integrating e-commerce platforms with AIoT systems and robots that are following consumers’ needs. Therefore, this proposed system diverts consumers who are moved by AIoT, while robotic manipulators replace human tasks to pick. To prove this idea, we implemented a modified YOLO (You Only Look Once) algorithm as a detection and localization tool for items purchased by consumers. At the same time, the modified YOLOv2 with data-driven mode was used for the process of taking goods from unstructured shop shelves. Our system performance is proven by experiments to meet the expectations in evaluating efficiency, speed, and convenience of the system in Society 5.0’s context.

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