轴心轨迹象限面积向量的轴承特征参数提取

Feature extraction of the bearing based on axle centre trail quadrant area vector

提取各典型状态下轴承轴心轨迹象限面积向量的信息熵,和多周期面积向量的重合度以及重合度标准差,分析在不同象限划分下,各特征参数对状态的敏感程度,通过改进特征参数计算方法,提高对状态的灵敏度。结合实验给出改进前后特征参数值与轴承状态的关系曲线,分析参数选择、象限划分及重合度计算方法在状态划分中的重要作用。

Information entropy, coincidence degree and its standard errors of axle centre trail quadrant area vector under different bearing states are extracted, the sensibility degree of the different parameters for the bearing states in different quadrant division is analyzed. By modifying the parameter compute method, the sensibility degree is improved. At last, the relation curve is gained in different quadrant division and different parameter compute methods, and the experiment demonstrates the importance of the parameter, quadrant division and the coincidence degree compute method in the parameter extraction.