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Criticality Analysis and Maintenance of Solar Tower Power Plants by Integrating the Artificial Intelligence Approach

Author

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  • Samir Benammar

    (Laboratoire Energétique—Mécanique & Ingénieries (LEMI), Université M’Hamed Bougara de Boumerdes, Boumerdes 35000, Algeria)

  • Kong Fah Tee

    (School of Engineering, University of Greenwich, Kent ME4 4TB, UK)

Abstract

Maintenance of solar tower power plants (STPP) is very important to ensure production continuity. However, random and non-optimal maintenance can increase the intervention cost. In this paper, a new procedure, based on the criticality analysis, was proposed to improve the maintenance of the STPP. This procedure is the combination of three methods, which are failure mode effects and criticality analysis (FMECA), Bayesian network and artificial intelligence. The FMECA is used to estimate the criticality index of the different elements of STPP. Moreover, corrections and improvements were introduced on the criticality index values based on the expert advice method. The modeling and the simulation of the FMECA estimations incorporating the expert advice method corrections were performed using the Bayesian network. The artificial neural network is used to predicate the criticality index of the STPP exploiting the database obtained from the Bayesian network simulations. The results showed a good agreement comparing predicted and actual criticality index values. In order to reduce the criticality index value of the critical elements of STPP, some maintenance recommendations were suggested.

Suggested Citation

  • Samir Benammar & Kong Fah Tee, 2021. "Criticality Analysis and Maintenance of Solar Tower Power Plants by Integrating the Artificial Intelligence Approach," Energies, MDPI, vol. 14(18), pages 1-27, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5861-:d:636708
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    References listed on IDEAS

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    1. Sergio Bemposta Rosende & Javier Sánchez-Soriano & Carlos Quiterio Gómez Muñoz & Javier Fernández Andrés, 2020. "Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants," Energies, MDPI, vol. 13(21), pages 1-23, November.
    2. Samir Benammar & Kong Fah Tee, 2020. "Failure probability analysis of heliostat systems," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 16(4), pages 342-366.
    3. Aránzazu Fernández-García & Adel Juaidi & Florian Sutter & Lucía Martínez-Arcos & Francisco Manzano-Agugliaro, 2018. "Solar Reflector Materials Degradation Due to the Sand Deposited on the Backside Protective Paints," Energies, MDPI, vol. 11(4), pages 1-20, March.
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    Cited by:

    1. María Luz Gámiz & Delia Montoro-Cazorla & María del Carmen Segovia-García & Rafael Pérez-Ocón, 2022. "MoMA Algorithm: A Bottom-Up Modeling Procedure for a Modular System under Environmental Conditions," Mathematics, MDPI, vol. 10(19), pages 1-19, September.
    2. Christian Spreafico & Agung Sutrisno, 2023. "Artificial Intelligence Assisted Social Failure Mode and Effect Analysis (FMEA) for Sustainable Product Design," Sustainability, MDPI, vol. 15(11), pages 1-16, May.
    3. Fausto Pedro García Márquez, 2022. "Maintenance Management in Solar Energy Systems," Energies, MDPI, vol. 15(10), pages 1-3, May.

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