Makine Öğrenmesi Tekniklerini Kullanarak Ultrasonik Su Sayacının Durağan Su Tespiti

Year 2021, Issue: 26 - Ejosat Special Issue 2021 (HORA), 477 - 481, 31.07.2021

Alkım Gökçen , Bahadır Yeşil

https://doi.org/10.31590/ejosat.961090

Abstract

Bu çalışma, akış hattının su ile dolu olduğu ve akışın sıfır olduğu durumlarda fiziksel bozucu etkilerden kaynaklı yanlış-pozitif ultrasonik sensör okumalarının sınıflandırma tabanlı tespit metodu sunulmaktadır. Özetlenen bozucu etkiler nedeniyle bu sayaç yanlış-pozitif okumaları yanlış faturalandırmaya sebep olabilir. Bu sorunu aşmak için, ultrasonik sensör ölçümleri farklı su akış hızlarında zamanserisi verileri olarak toplandı. Verilerin nümerik ve istatistiksel ölçümleri, bir giriş/çıkış ilişkisi kurmak adına hesaplanmıştır. Bu nedenle, nitelik çıkarma işlemleri yapılmıştır. Modelleme fazında akışın olup/olmadığı her iki durum için veriler ve denk geldikleri nitelikler etiketlenmiştir. Lojistik regresyon (LR), Destek Vektör Makineleri (DVM), ve lineer diskriminant analizi (LDA) algoritmaları akış durumlarını sınıflandırmak amacıyla MATLAB ortamında kullanılmıştır. Model başarımları doğruluk, hassasiyet, özgüllük ve kesinlik performans metrikleri ile karşılaştırılmıştır. Ayrıca algoritmaların gerçek sistem çalışmalarında uygulanabilirliği model kompleksiteleri incelenerek tartışılmıştır. Seçilen model bir su sayacına uygulanmış, ve tüketim ölçümleri algoritmanın uygulanmadığı bir başka sayaç ile ayı test masasında karşılaştırılmıştır. Fiziksel bozucu etkilerin benzetimi için akış hattı düzenli olarak titreştirilmiştir. Sayaçların tüketim kayıtları tablolanmış, model performansları tartışılmış ve sonuçlar paylaşılmıştır. Sonuçlara göre, radyal tabanlı DVM algoritması bütün metrikler anlamında en iyi sonucu vermiştir. Model karmaşıklığı açısından oldukça basit olan LR algoritması gerçek sistem çalışması için seçilmiştir.

Keywords

Akış ölçümü, Yanlış pozitif tespiti, Ultrasonik sensör ölçümü, Sınıflandırma, Makine öğrenmesi

Zero Flow Rate Detection of Ultrasonic Water Meter Using Machine Learning Techniques

Abstract

This paper presents a classification-based method to detect false-positive ultrasonic sensor measurements when the flow rate is zero while the pipeline is full of water caused by the physical disturbances in flow metering. Due to the outlined disturbances, these false-positive readings of the meters may cause the wrong billing. To overcome this problem, ultrasonic sensor measurements are collected as timeseries data at variously different water flow rates. Numerical and statistical measures of the timerseries are computed to construct an input-output relation. Hence, the feature extraction process is performed. For the modeling phase, both zero flow rate and non-zero flow rate parts of the dataset, and its corresponding features are labeled. Logistic Regression (LR), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) algorithms are employed to classify the flow status in the MATLAB environment. Model performances are compared in terms of accuracy, sensitivity, specificity, and precision. For the investigation of the availability of the implementation of an embedded system, model complexities are discussed. Selected model parameters are embedded in a water meter, and consumption values are compared to a water meter without the detection algorithm in the same test bench underfilled pipeline with zero flow rate condition. To simulate the physical disturbance conditions, and observe the effect of the false-positive detection algorithm on flow metering, the flow pipeline is vibrated periodically. Consumption loggings of the water meters are tabled, model performance results are discussed, and test results are shared. According to the results, the radial basis kernel SVM algorithm performs better in terms of all metrics. LR algorithm is employed for the real plant experiment when its model complexity is considered.

Keywords

Flow Metering, False positive detection, Ultrasonic sensor measurement, Classification, Machine Learning.

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