Leak detection of pipeline using a hybrid of Neural-Adaptive Tabu Search algorithm

Author

Sornmuang, S. ; Suwatthikul, J. ; Thirachai, S.

Author_Institution

Nat. Electron. & Comput. Technol. Center, Pathumthani, Thailand

fYear

2014

fDate

22-25 Oct. 2014

Firstpage

531

Lastpage

534

Abstract

This paper presents a new hybrid of Neural-Adaptive Tabu Search (NATS) for leakage detection in pipelines. The proposed cooperative algorithms are formed from Artificial Neural Network (ANN) and Adaptive Tabu Search (ATS). The article shows comparison studies of the ANN and NATS algorithms. The search performance evaluation is performed on the standard benchmark from University of California at Irvine (UCI) Machine Learning Repository. The experiment uses water leakage signals from a field-test yard. The results show that the leaking pipeline can be efficiently detected.

Keywords

acoustic signal processing; leak detection; learning (artificial intelligence); neural nets; pipelines; search problems; ANN algorithm; ATS algorithm; Irvine; UCI machine learning repository; University of California; artificial neural network algorithm; cooperative algorithm; field-test yard; hybrid NATS algorithm; hybrid neural-adaptive tabu search algorithm; pipeline leak detection; search performance evaluation; standard benchmark; water leakage signals; Accuracy; Acoustics; Artificial neural networks; Asphalt; Breast; Concrete; Iris; Neural-Adaptive Tabu Search; hybrid algorithms; leak detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation and Systems (ICCAS), 2014 14th International Conference on

Conference_Location

Seoul

ISSN

2093-7121

Print_ISBN

978-8-9932-1506-9

Type

conf

DOI

10.1109/ICCAS.2014.6987836

Filename

6987836