Turbine Blade Failure Diagnosis Based on Relevance Vector Machine Optimized by Genetic Algorithm

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.