基于时间序列的参数模型和Fisher判别分析的滚动轴承故障诊断方法

Fault Diagnosis of Rolling Bearing Based on Parameter Model of Time-Series and Fisher Discriminant Analysis

提出了基于时间序列参数模型和Fisher判别分析的滚动轴承故障诊断方法。该方法通过对轴承振动信号建立自回归模型,将自回归模型的特征参数作为特征向量,然后采用Fisher判别分析方法对轴承状态进行分类与识别,实验结果验证了所用方法的有效性。

In this paper, a fault diagnosis approach to rolling bearing based on parameter model of time - series and Fisher Discriminant Analysis （FDA） is proposed. Firstly, the AR model of the vibration signal of rolling bearing is established, the auto- regressive（AR） parameters are regarded as the feature vectors. Then, the states of rolling bearing are identified and classified by FDA. The test result shows us the availability of the method.