基于数学形态滤波的齿轮故障特征提取方法

针对齿轮故障特征的提取问题,提出一种根据信号形态特征对齿轮故障信号进行形态滤波的新方法.形态滤波是一种新的非线性滤波方式,可以有效地提取出信号的边缘轮廓以及信号的形状特征.对Lorenz信号进行不同结构元素的数学形态滤波处理,证实形态滤波对抑制信号噪声、保留信号非线性特征方面的作用.采用长度为齿轮冲击周期长度的0.6～0.8倍的扁平结构元素,对齿轮断齿故障振动信号进行形态闭运算处理,并对滤波后的信号进行频谱分析.结果表明,利用形态滤波可以从齿轮断齿信号中成功提取隐含在噪声中的冲击故障特征.

APPROACH TO EXTRACTING GEAR FAULT FEATURE BASED ON MATHEMATICAL MORPHOLOGICAL FILTERING

To extract fault feature of gear, a novel approach is proposed according to the signal characteristics based on morphological filtering. As a nonlinear filtering algorithm for digital signal processing, morphological filtering is able to identify the feature of fringe and shape of the signal. Lorenz signal is processed by mathematical morphological filtering via various structuring elements, and the effect of noise reduction and nonlinear feature reservation of morphological filtering is validated. The vibration signal of gear teeth broken is processed by morphological closing operation via the flat structuring elements, and the length of the structuring elements is 0.6 to 0.8 times to the length of gear impact period. Then, the filtered signal is analyzed by Fourier frequency spectrum. The results show that the impact feature, which can not be identified from noisy data directly, is successfully extracted by morphological filtering.