一种主轴系统故障识别方法

故障识别是确定故障类型的重要方式。传统方法不能直观识别故障类型，忽略了水平和垂直方向的信息之间的关系，很难准确提取故障特征。二维全息谱融合了水平和垂直方向的振动信息，反映了一个支承面上转子的振动情况。但在某些情况下不能准确识别主要故障，无法通过分倍频、工频和高倍频的椭圆信息确定故障类型。选择流形学习的 LE 算法与全息谱技术结合，弥补了二维全息谱算法的缺陷，提高了流形学习处理信号的优越性。通过实验验证了方法的准确性。

One Method of Fault Identification for Spindle System

Fault identification is an important way to determine the form of fault. Traditional methods can not intuitively identi-fy fault types, ignoring the relationship between the information of the horizontal and vertical direction and it is difficult to ac-curately extract the fault feature. Two dimensional holographic spectrum has blend the vibration of horizontal and vertical di-rection information, reflecting the vibration of the rotor on a supporting surface. But in some cases it can not accurately identify major failure and determine the failure types through elliptic information of points frequency doubling, power frequency and high frequency. Choosing the manifold learning LE algorithm and combined with holographic spectrum technology, making up for the defects of the two dimensional holospectrum algorithm and improving the advantages of manifold learning signal process-ing. the correct result have been got by experiment.