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Research Papers

Water Leakage Detection for Complex Pipe Systems Using Hybrid Learning Algorithm Based on ANFIS Method

[+] Author and Article Information
Barış Can Yalçın

Mechatronics Engineering Department,
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: bcyalcin@yildiz.edu.tr

Cihan Demir

Mechanical Engineering Department,
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: cdemir@yildiz.edu.tr

Murat Gökçe

İstanbul Water and Sewerage Administration,
Kağıthane, İstanbul 34406, Turkey
e-mail: 1muratgkc@gmail.com

Ahmet Koyun

Mechatronics Engineering Department,
Yıldız Technical University,
Beşiktaş, İstanbul 34349, Turkey
e-mail: koyun@yildiz.edu.tr

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received November 17, 2017; final manuscript received April 19, 2018; published online July 3, 2018. Assoc. Editor: John Michopoulos.

J. Comput. Inf. Sci. Eng 18(4), 041004 (Jul 03, 2018) (10 pages) Paper No: JCISE-17-1275; doi: 10.1115/1.4040130 History: Received November 17, 2017; Revised April 19, 2018

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 and academia due to the financial cost related with public health risks, environmental responsibility, and energy efficiency. In this paper, to find a reliable and 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.

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Figures

Grahic Jump Location
Fig. 1

The cells and the sections on the axonometric view of the experimental setup

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Fig. 2

Adaptive neuro fuzzy inference system structure

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Fig. 3

Initial schemes of adaptive membership functions

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Fig. 4

Artificial leakage points for configuration-1, configuration-2, and configuration-3; each artificial leakage point is denoted by a number and color, and each color represents a different section

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Fig. 5

Training data set H for the artificial leakage at branch-1

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