摘要
提出一种基于高斯基函数小脑模型神经网络(CMAC)的汽轮发电机故障诊断新方法,为了达到更高的精度和更好的泛化能力,该方法以高斯函数作为CMAC神经网络的基函数,针对发电机的机电耦合特性,将发电机机电综合特征作为神经网络的训练样本输入,经MATLAB仿真得到了完全正确的诊断结果,收敛速度快,精度高,可以满足在线监控的要求。通过比较学习率和泛化常数取值不同时CMAC网络的训练结果,分析了学习率和泛化常数对该网络的影响。
Based on a CMAC neural network with Gauss basis functions,a novel method was proposed for fault diagnosis of a turbo-generator.In order to achieve higher precision and better generalization ability,this method used Gauss basis functions in the CMAC neural network.Aiming at electrical and mechanical coupling characteristics of the generator,its integrated mechanical and electrical features as a neural network training sample were imputted.Through MATLAB simulation,the completely correct diagnosis results with higher convergence speed and accuracy,which met the requirements of on-line monitoring,were obtained.By comparing the CMAC network training results using the different values of the learning rate and generalization constant at the same time,the influence of the learning rate and generalization constant on the neural network was analyzed.
出处
《振动与冲击》
EI
CSCD
北大核心
2010年第4期84-87,134,共5页
Journal of Vibration and Shock
基金
国家自然科学基金项目(50677017)
中央高校基本科研业务费专项资金资助项目(09MG30)
关键词
小脑模型神经网络(CMAC)
高斯基函数
发电机
故障诊断
机电综合特征
cerelbllar model articulation controller(CMAC)
gauss basis function
generator
fault diagnosis
integrated mechanical and electrical features
