基于高斯基函数CMAC神经网络的发电机故障诊断方法

万书亭;何鹏;赵松杰

振动与冲击 ›› 2010, Vol. 29 ›› Issue (4) : 84-87,1.

PDF(678 KB)
PDF(678 KB)
振动与冲击 ›› 2010, Vol. 29 ›› Issue (4) : 84-87,1.
论文

基于高斯基函数CMAC神经网络的发电机故障诊断方法

  • 万书亭1;何鹏1;赵松杰2
作者信息 +

Fault Diagnosis of Generator Using CMAC Neural Network with Gauss Basis Function

  • Wan Shuting1; He Peng1; Zhao Songjie2
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文章历史 +

摘要


本文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。





Abstract

Based on CMAC neural network with Gauss basis function, a novel method was proposed for fault diagnosis of turbo-generator. In order to achieve higher precision and better generalization ability, this method used Gauss basis function in CMAC neural network. Because electrical and mechanical coupling characteristics of the generator, made integrated mechanical and electrical characteristics as a neural network training input samples. Through MATLAB simulation, we got completely correct diagnosis results with the high convergence speed and accuracy that met the requirements of on-line monitoring. By comparing CMAC network training results when used the different values of the learning rate and generalization constant value at the same time, the study analyzed the influence of the learning rate and generalization constant to the neural network.

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

Key words

Cerelbllar Model Articulation Controller(CMAC) / Gauss basis function / generator / fault diagnosis / integrated mechanical and electrical characteristics

引用本文

导出引用
万书亭;何鹏;赵松杰. 基于高斯基函数CMAC神经网络的发电机故障诊断方法[J]. 振动与冲击, 2010, 29(4): 84-87,1
Wan Shuting;He Peng;Zhao Songjie. Fault Diagnosis of Generator Using CMAC Neural Network with Gauss Basis Function[J]. Journal of Vibration and Shock, 2010, 29(4): 84-87,1

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