Fully automatic AI-based leak detection system

Wojciech Tylman, Jakub Kolczyński and George J. Anders

Energy, 2010, vol. 35, issue 9, 3838-3848

Abstract: This paper presents a fully automatic system intended to detect leaks of dielectric fluid in underground high-pressure, fluid-filled (HPFF) cables. The system combines a number of artificial intelligence (AI) and data processing techniques to achieve high detection capabilities for various rates of leaks, including leaks as small as 15 l per hour. The system achieves this level of precision mainly thanks to a novel auto-tuning procedure, enabling learning of the Bayesian network – the decision-making component of the system – using simulated leaks of various rates. Significant new developments extending the capabilities of the original leak detection system described in [1] and [2] form the basis of this paper. Tests conducted on the real-life HPFF cable system in New York City are also discussed.

Keywords: Bayesian networks; Neural networks; Pipe type cables; HPFF (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:35:y:2010:i:9:p:3838-3848

DOI: 10.1016/j.energy.2010.05.038

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