基于小波遗传神经网络的大型直线振动筛故障诊断  

Large-scale Line Vibrating Screen Fault Diagnosis Based on Wavelet Genetic Neural Network

作　　者：张凤蕊[1] 王志奎[1] 

机构地区：[1]南阳理工学院电子与电气工程学院,河南南阳473004

出　　处：《煤矿机械》2012年第9期259-261,共3页Coal Mine Machinery

摘　　要：以大型直线振动筛侧帮裂纹为研究对象,利用小波分析对振动信号进行降噪处理和故障特征提取,设计系统故障诊断的BP神经网络,并用遗传算法对网络结构、参数和学习规则进行优化。通过样本训练和测试,这种小波遗传神经网络具有较高的故障识别能力、分类精度和速度。The paper implements vibration signals noise reduction and fault characteristic extraction using wavelet analysis, designs system BP neural network of fault diagnosis and optimizes net construction ,parameters and learning rule by genetic algorithm taking large-scale line vibrating screen as research object. After sample training and test, it shows that this kind of wavelet genetic neural network has high fault recognition ability, classification precision and speed.

关 键 词：故障诊断 振动筛 小波分析 神经网络 遗传算法 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置] TD452[自动化与计算机技术—控制科学与工程]