Probabilistic multiple model neural network based leak detection system:experimental study


This paper presents an effective decision making system for leak detection based on multiple generalized linear models and clustering techniques. The training data for the proposed decision system is obtained by setting up an experimental pipeline fully operational distribution system. The system is also equipped with data logging for three variables; namely, inlet pressure, outlet pressure, and outlet flow. The experimental setup is designed such that multi-operational conditions of the distribution system, including multi pressure and multi flow can be obtained. We then statistically tested and showed that pressure and flow variables can be used as signature of leak under the designed multi-operational conditions. It is then shown that the detection of leakages based on the training and testing of the proposed multi model decision system with pre data clustering, under multi operational conditions produces better recognition rates in comparison to the training based on the single model approach. This decision system is then equipped with the estimation of confidence limits and a method is proposed for using these confidence limits for obtaining more robust leakage recognition results.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Mathematics
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: multiple models,negative pressure wave,paired t-test,pipeline leak detection,uncertainty,Industrial and Manufacturing Engineering,Control and Systems Engineering,Safety, Risk, Reliability and Quality,Management Science and Operations Research,Chemical Engineering(all),Energy Engineering and Power Technology,Food Science
Publication ISSN: 0950-4230
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2015-07
Published Online Date: 2015-05-07
Authors: Burhan Abdulla, Mohammad
Herzallah, Randa (ORCID Profile 0000-0001-9128-6814)

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