基于最小二乘支持向量机的矿用胶带机滚动轴承故障识别

Mine Sealing-tape Machine Rolling Bearing Failure Identification Based on Least Squares Support Vector Machine

提出利用最小二乘支持向量机方法进行矿用胶带机滚动轴承故障识别方法,利用小波包的分解方法提取检测信号的故障信息,并将其作为最小二乘支持向量机的输入量,将样本的常见故障类型作为输出量,对样本的输入量和输出量进行不断的训练学习,得到最小二乘支持向量机模型,利用该模型进行胶带机滚动轴承故障识别。研究结果表明:基于最小二乘支持向量机模型的计算结果与测试样本拟合精度较高,可以用于进行矿用胶带机滚动轴承故障识别。

Mine sealing-tape machine rolling bearing failure identification method based on least squares support vector machine was put forward,failure information of detection signal was extracted by wavelet packet decomposition,and then as input variable of least squares support vector machine,normal failure type of sample as output variable,and then input and output variables of the sample were trained continuous,so least squares support vector machine model was obtained,and then failure identification of mine sealing-tape machine rolling bearing was proceed by the model.The results showed that calculation results based on least squares support vector machine had higher fitting precision with testing sample,and it could be used for mine sealing-tape machine rolling bearing failure identification.