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

Samir Benammar and Kong Fah Tee
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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

Energies, 2021, vol. 14, issue 18, 1-27

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.

Keywords: criticality analysis; solar tower power plants; maintenance; artificial intelligence; bayesian network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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