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Multi-fault situation assessment and diagnosis method of wi..:

, In: 12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022),

Ge, X. ; Bi, J. ; Wang, G.... - p. None , 2022

Link: https://doi.org/10.1049/icp.2022.3079

RT 
T1 <cTitle>12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022)</cTitle>: T1 <Title>Multi-fault situation assessment and diagnosis method of wind turbine blades based on fault tree and Bayesian network</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10110593&Exemplar=1&LAN=DE
				
A1 Ge, X.
A1 Bi, J.
A1 Wang, G.
A1 Jiao, J.-M.
A1 Ma, H.
A1 Zhou, D.-C.
YR 2022
K1 belief networks
K1 blades
K1 fault diagnosis
K1 fault trees
K1 fuzzy set theory
K1 probability
K1 wind turbines
K1 Bayes formula
K1 Bayesian network fault diagnosis model
K1 conversion algorithm
K1 diagnosis method
K1 eolian power generation systems
K1 expert knowledge
K1 fault node
K1 fault statistical data
K1 fault tree model
K1 fault tree structure model
K1 fuzzy comprehensive evaluation
K1 logic gates
K1 multifault situation assessment
K1 posterior probability
K1 prior probability
K1 root nodes
K1 structural importance
K1 system failure
K1 system reliability
K1 wind turbine blade
SP None
LK http://dx.doi.org/https://doi.org/10.1049/icp.2022.3079
DO https://doi.org/10.1049/icp.2022.3079
SF ELIB - SuUB Bremen

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