Title :
Turbine Blade Failure Diagnosis Based on Relevance Vector Machine Optimized by Genetic Algorithm
Author :
Xiao Yihan ; Zhang Mingyao ; Chen Liwei ; Li Mingkui
Author_Institution :
Coll. of Inf. & Commun. Eng., Harbin Eng. Univ., Harbin, China
Abstract :
This paper uses genetic algorithm to optimize the relevance vector machine algorithm to extract the characteristic vector of fault classification, and by contrasting with relevance vector machine, the support vector machine and BP neural network method, it is know that the relevance vector machine optimized by genetic algorithm (ga) can more accurately classify the fault type of conclusion.
Keywords :
backpropagation; blades; fault location; gas turbines; genetic algorithms; mechanical engineering computing; support vector machines; BP neural network method; fault classification; genetic algorithm; optimization; relevance vector machine algorithm; support vector machine; turbine blade failure diagnosis; Computational intelligence; BP neural network; fault classification; genetic algorithm; relevance machine; support vector machine;
Conference_Titel :
Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
Print_ISBN :
978-1-4799-7004-9
DOI :
10.1109/ISCID.2014.270
