基于自适应多尺度自互补Top-Hat变换的轴承故障增强检测

Enhanced Detection of Bearing Faults Based on Adaptive Multi-scale Self-complementary Top-Hat Transformation

针对实际工程中滚动轴承冲击性故障特征难以提取的问题,提出一种自适应多尺度自互补Top-Hat(Adaptive multi-scale self-complementary Top-Hat,AMSTH)变换方法用于轴承故障的增强检测。自互补Top-Hat变换在消除信号中背景噪声的同时,能有效增强故障振动信号的冲击特性,而构造的多尺度自互补Top-Hat变换方法,可以较有效地兼顾抗噪性能和信号的细节保持。在分析形态学滤波的基础上,提出采用特征幅值能量比(Feature amplitude energy radio,FAER)的方法自适应确定最优结构元素的尺度,并应用于轴承的故障增强检测。通过对仿真信号和实测轴承滚动体、内圈故障信号进行分析,结果表明该方法可有效增强滚动轴承的故障检测,并且在运算效率和提取效果方面优于基于信噪比标准的多尺度形态学开-闭和闭-开组合变换方法。

Aiming at the difficulty of extracting impulsive fault feature of rolling element bearings in practical engineering,a novel method named adaptive multi-scale self-complementary Top-Hat（AMSTP） transformation is proposed to enhance detection of bearing faults. It can enhance the impulsiveness of the bearing fault vibration signal and depress strong background noise,and constructing multi-scale is better to depress noise and retain detail of signal. The most optimal structure element（SE） scale is selected by using a novel method of feature amplitude energy radio（FAER）,and it is applied in detecting fault feature of impulsive signal successfully. The performance of the proposed method is validated by both simulated signal and vibration signals of defective rolling element bearings with ball and inner faults. In addition the method could achieve better effect on feature extraction and have more operation efficiency than open-closing and close-opening combined morphological method based on signal noise ratio（SNR） criterion.