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research-article

Water Leakage Detection for Complex Pipe Systems using Hybrid Learning Algorithm based on ANFIS Method

[+] Author and Article Information
Baris Yalçin

Mechatronics Engineering Department, Yıldız Technical University, Beşiktaş İstanbul 34349
bcyalcin@yildiz.edu.tr

Cihan Demir

Mechanical Engineering Department, Yıldız Technical University, Beşiktaş İstanbul 34349
cdemir@yildiz.edu.tr

Murat Gokce

İstanbul Water and Sewerage Administration, Kağıthane, İstanbul 34406
1muratgkc@gmail.com

Ahmet Koyun

Mechatronics Engineering Department, Yıldız Technical University, Beşiktaş İstanbul 34349
koyun@yildiz.edu.tr

1Corresponding author.

ASME doi:10.1115/1.4040130 History: Received November 17, 2017; Revised April 19, 2018

Abstract

In most city water distribution systems, a considerable amount of water is lost because of leaks occurring in pipes. Moreover, an unobservable fluid leakage fault that may occur in a hazardous industrial system, such as nuclear power plant cooling process or chemical waste disposal, can cause both environmental and economical disasters. This situation generates crucial interest for industry & academia due to the financial cost related with public health risks, environmental responsibility and energy efficiency. In this paper, to find a reliable & economic solution for this problem, Adaptive Neuro Fuzzy Inference System (ANFIS) method which consists of backpropagation and least-squares learning algorithms is proposed for estimating leakage locations in a complex water distribution system. The hybrid algorithm is trained with acceleration, pressure and flow rate data measured through the sensors located on some specific points of the complex water distribution system. The effectiveness of the proposed method is discussed comparing the results with the current methods popularly used in this area.

Copyright (c) 2018 by ASME
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