基于SOM神经网络的矿井提升机减速器齿轮故障诊断  

Malfunction Diagnosis to Gear of Reducer of Mine Hoist Based on SOM Neural Network

作　　者：李春华[1] 肖洋[1] 刘绍东[1] 

机构地区：[1]黑龙江科技学院电气与信息工程学院,黑龙江哈尔滨150027

出　　处：《矿山机械》2007年第8期92-94,共3页Mining & Processing Equipment

摘　　要：在分析自组织特征映射神经网络(SOM)的结构和学习算法的基础上,利用自组织特征映射神经网络建立了提升机减速器齿轮故障诊断模型。该网络模型效率高,无需监督,能自动对输入模式进行聚类。应用Matlab神经网络工具箱进行仿真。仿真结果表明:自组织特征映射神经网络有较强的聚类功能,用于减速器齿轮故障诊断是准确和可靠的。Based on the analysis to the structure and learning algorithm of self-organizing feature mapping neural network.( SOM ), the paper established the malfunction diagnosis model for gear of reducer of mine hoist by SOM. The model possessed the advantage of high efficiency and free from monitor, and could automatically cluster for input patterns. MATLAB neural network toolbox was applied to simulate, the result showed that SOM had strong clustering function, was accurate and reliable for malfunction diagnosis to reducer gear.

关 键 词：齿轮 自组织特征映射神经网络(SOM) 故障诊断 

分 类 号：TD534[矿业工程—矿山机电]