Research on fault diagnosis method of steam turbine generator based on analysis of air gap magnetic density characteristics

This article focuses on the inclined eccentricity fault of steam turbine generators, analyzes the distribution characteristics of air gap magnetic density under different inclined eccentricity states, extracts key characteristic parameters, and proposes a fault diagnosis method based on the analysis of air gap magnetic density characteristics. By combining particle swarm optimization (PSO) algorithm with radial basis function (RBF) neural network, an intelligent diagnostic model was constructed to achieve efficient identification of inclined eccentricity faults. The results indicate that this method has high diagnostic accuracy and provides a scientific basis for the operation and maintenance of steam turbine generators.