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

被引:9
|
作者
Benammar, Samir [1 ]
Tee, Kong Fah [2 ]
机构
[1] Univ MHamed Bougara Boumerdes, Lab Energet Mecan & Ingn LEMI, Boumerdes 35000, Algeria
[2] Univ Greenwich, Sch Engn, Chatham ME4 4TB, Kent, England
关键词
criticality analysis; solar tower power plants; maintenance; artificial intelligence; bayesian network; RELIABILITY-ANALYSIS; FAILURE ANALYSIS; NEURAL-NETWORK; VALVE; COLLECTORS; HELIOSTAT;
D O I
10.3390/en14185861
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:26
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