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Fully automatic AI-based leak detection system

Author

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  • Tylman, Wojciech
  • Kolczyński, Jakub
  • Anders, George J.

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.

Suggested Citation

  • Tylman, Wojciech & Kolczyński, Jakub & Anders, George J., 2010. "Fully automatic AI-based leak detection system," Energy, Elsevier, vol. 35(9), pages 3838-3848.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:9:p:3838-3848
    DOI: 10.1016/j.energy.2010.05.038
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    References listed on IDEAS

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    1. Tylman, Wojciech & Anders, George J., 2006. "Application of probabilistic networks for decision support in power system analysis," Energy, Elsevier, vol. 31(14), pages 2874-2889.
    2. Gale, John & Davison, John, 2004. "Transmission of CO2—safety and economic considerations," Energy, Elsevier, vol. 29(9), pages 1319-1328.
    3. Gerboni, R. & Salvador, E., 2009. "Hydrogen transportation systems: Elements of risk analysis," Energy, Elsevier, vol. 34(12), pages 2223-2229.
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    Cited by:

    1. He, Guoxi & Li, Yansong & Huang, Yuanjie & Sun, Liying & Liao, Kexi, 2019. "A framework of smart pipeline system and its application on multiproduct pipeline leakage handling," Energy, Elsevier, vol. 188(C).
    2. Brkovic, Aleksandar & Gajic, Dragoljub & Gligorijevic, Jovan & Savic-Gajic, Ivana & Georgieva, Olga & Di Gennaro, Stefano, 2017. "Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery," Energy, Elsevier, vol. 136(C), pages 63-71.
    3. Du, Zhimin & Chen, Ling & Jin, Xinqiao, 2017. "Data-driven based reliability evaluation for measurements of sensors in a vapor compression system," Energy, Elsevier, vol. 122(C), pages 237-248.

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