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Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems

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  • Biagetti, Tatiana
  • Sciubba, Enrico

Abstract

This paper presents a critical and analytical description of an ongoing research program aimed at the implementation of an expert system capable of monitoring, through an Intelligent Health Control procedure, the instantaneous performance of a cogeneration plant. An application has been tested on a real plant, located on the grounds of the ENEA-Casaccia Energy Laboratories. The expert system, denominated PROMISE as the Italian acronym for PROgnostic and Intelligent Monitoring Expert System, generates, in real time and in a form directly useful to the plant manager, information on the existence and severity of faults, forecasts on the future time history of both detected and likely faults, and suggestions on how to control the problem.

Suggested Citation

  • Biagetti, Tatiana & Sciubba, Enrico, 2004. "Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems," Energy, Elsevier, vol. 29(12), pages 2553-2572.
    • Handle: RePEc:eee:energy:v:29:y:2004:i:12:p:2553-2572
    DOI: 10.1016/j.energy.2004.03.031
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    Cited by:

    1. Abdenour Soualhi & Mourad Lamraoui & Bilal Elyousfi & Hubert Razik, 2022. "PHM SURVEY: Implementation of Prognostic Methods for Monitoring Industrial Systems," Energies, MDPI, vol. 15(19), pages 1-24, September.
    2. Chung, Mo & Park, Hwa-Choon, 2010. "Development of a software package for community energy system assessment – Part I: Building a load estimator," Energy, Elsevier, vol. 35(7), pages 2767-2776.
    3. Silva, J.A.M. & Venturini, O.J. & Lora, E.E.S. & Pinho, A.F. & Santos, J.J.C.S., 2011. "Thermodynamic information system for diagnosis and prognosis of power plant operation condition," Energy, Elsevier, vol. 36(7), pages 4072-4079.
    4. Vazquez, Luis & Blanco, Jesús María & Ramis, Rolando & Peña, Francisco & Diaz, David, 2015. "Robust methodology for steady state measurements estimation based framework for a reliable long term thermal power plant operation performance monitoring," Energy, Elsevier, vol. 93(P1), pages 923-944.
    5. Zhou, Dengji & Zhang, Huisheng & Weng, Shilie, 2014. "A novel prognostic model of performance degradation trend for power machinery maintenance," Energy, Elsevier, vol. 78(C), pages 740-746.
    6. Ahmad Y. Al Rashdan & Hany S. Abdel-Khalik & Kellen M. Giraud & Daniel G. Cole & Jacob A. Farber & William W. Clark & Abenezer Alemu & Marcus C. Allen & Ryan M. Spangler & Athi Varuttamaseni, 2022. "A Qualitative Strategy for Fusion of Physics into Empirical Models for Process Anomaly Detection," Energies, MDPI, vol. 15(15), pages 1-20, August.

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