文献类型：期刊文献

中文题名：基于补偿模糊神经网络的核电百万千瓦级汽轮发电机的故障诊断

英文题名：Fault diagnosis for the million-kilowatt-class nuclear power turbo-generator based on compensatory fuzzy neural network

作者：温孟振[1];秦宗慧[1]

机构：[1]华东理工大学机械与动力工程学院,上海200237

年份：2009

卷号：22

期号：6

起止页码：98

中文期刊名：机械研究与应用

外文期刊名：Mechanical Research & Application

基金：上海市重点攻关项目(148109)

语种：中文

中文关键词：核电百万千瓦级汽轮发电机;补偿模糊神经网络;故障诊断;补偿模糊运算

外文关键词：million-kilowatt-class nuclear power turbo-generator; compensatory fuzzy neural network; fault diagnosis; compensatory fuzzy operators

摘要：在独特的、自行设计的多功能核电百万千瓦级汽轮发电机试验台上,人工模拟了多种故障,选择反映其故障状态的不同频段参数组成特征向量,利用补偿模糊神经网络(CFNN)系统进行故障诊断分析。由于网络引入了补偿模糊神经元,使其容错率更高,系统更稳定。仿真实验证明该模型在智能诊断中具有收敛速度快,诊断精度高,而且适应性强等优点。
In this article,the different frequency band parameters and measures are selected to form the characteristic vector by making use of the compensation fuzzy neural network（CFNN）system to simulate and diagnose many kinds of faults on the unique multifunction million-kilowatt-class nuclear power turbo-generator test-bed.Because the compensation fuzzy neural ceil is introduced into the network,it can lead to the network fault-tolerant rate to be higher and the system to be more stable.The simulating experiment has proved that this model has many merits,such as： the convergence speed is quick,the diagnosis precision is high,and compatibility is strong and so on.