BP-ART2网络设计及其在故障诊断中的应用  

Design on BP-ART2 Network and the Applicaion on Fault Diagnosis

作　　者：阳小燕[1,2] 王靖[2] 李学军[2] 

机构地区：[1]中南大学机电工程学院,长沙410083 [2]湖南科技大学机电工程学院,湖南省湘潭市411201

出　　处：《微计算机信息》2009年第34期1-3,共3页Control & Automation

基　　金：湖南省科学技术厅科技计划项目;基金申请人:王靖;项目名称:基于多传感器信号融合的旋转机械故障诊断关键技术研究;基金颁发部门:湖南省科学技术厅(06FJ3044)

摘　　要：BP神经网络的非线性映射特性在故障诊断领域得到了广泛应用,但对于新增故障模式缺乏有效的识别,而ART2自适应共振网络能有效增添新的故障模式,却不能对输入量进行特征降维约简,而输入量太多时耗费时间太长,本文综合BP神经网络与ART2自适应共振网络的优点,研究了改进型BP-ART2神经网络故障诊断方法,在ART2结构的输入层增加隐层,通过非线性映射实现特征量降维约简,从而提高ART2神经网络的诊断效率。通过旋转机械故障诊断结果表明,该方法能够有效地实现特征约简和故障聚类。BP neural networks has already been applied into diagnosis field,it can realize the nonlinearity mapping fault diagnosis,but can not identificate the new fault pattern effectivly. ART2 has an effect to increase the new fault pattern,but can not reduce the input characteristic dimension,when input amount is too large,it will consume too long time. Synthesized the merit of BP and ART2 networks,an improved BP-ART2 neural network fault diagnosis method has been reaserched,adding a hidden layer in input layer of ART2 structure,reduceing the input characteristic dimension by nonlinearity mapping,improving diagnosis efficiency of ART2 neural networks thereby. The experiment indicate in fault diagnosis of rotating machinery,the method can reduce the characteristic dimen-sion and realize fault clustering effectively.

关 键 词：BP网络 ART2网络 故障诊断 旋转机械 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]