基于局部特征尺度分解和形态学分形维数的滚动轴承故障诊断方法

提出了一种基于局部特征尺度分解与形态学分形维数的滚动轴承故障诊断方法。首先采用局部特征尺度分解方法将机械故障信号分解为若干个内禀尺度分量,然后利用形态学分形维数计算包含故障特征分量的分形维数,将得到的分形维数作为特征量判别信号故障的状态,实验结果表明基于局部特征尺度分解与形态学分形维数的故障诊断方法能够有效识别滚动轴承的内圈故障、外圈故障、滚动体故障和正常状态,实现滚动轴承故障诊断。

Rolling Bearing Fault Diagnosis Based on LoCal CharaCterist-sCale DeComposition and MorphologiCal FraCtal Dimension

A rolling bearing fault diagnosis method based on the morphological fractal dimension and Local Characterist-scale Decomposition is proposed. Firstly,Local Characterist-scale Decomposition is used to decompose the mechanical fault signals into a set of Intrinsic scale components,and then the morphological fractal dimension of Intrinsic scale components which contain the Intrinsic scale component characteristics is calculated. This is obtained as a characteristic parameter to judge the signal fault types. The experimental results that the proposed method based on the morphological fractal dimension and Local Characterist-scale Decomposition can realize different signal states(inner fault, outer race fault,rolling element fault and normal)about the bearing fault and the rolling fault diagnosis effectively.