局部独立分量分析降噪算法在轴承故障诊断中的应用  

The De-noising Algorithm Based on Local Independent Component Analysis and Its Application on Bearing Fault Diagnosis

作　　者：肖涵[1] 李友荣[1] 吕勇[1] 

机构地区：[1]武汉科技大学机械自动化学院,湖北武汉430081

出　　处：《矿山机械》2008年第20期14-16,共3页Mining & Processing Equipment

基　　金：国家自然科学基金资助(50705069)

摘　　要：提出一种局部独立分量分析降噪算法,采用该算法对 Lorenz 仿真信号进行降噪实验,结果表明局部独立分量分析降噪算法的降噪效果优于全局投影降噪算法的降噪效果,将该算法用于低速重载轴承的故障诊断,准确判断出了轴承存在的故障。A de-noising algorithm based on local independent component analysis is introduced. With this algorithm, a de-noising experiment on Lorenz simulation signals is done. The result shows that the noise reduction effect of local independent component analysis is better than that of global projection algorithm. The fault of low speed and heavy loading bearings is identified accurately by the proposed method.

关 键 词：降噪 局部独立分量分析 轴承 故障诊断 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]