谐波分解结合自互补Top-Hat变换的轴承微弱故障特征提取方法

针对轴承早期故障特征微弱，极易被背景噪声淹没而不能及时检测的问题，本文提出了基于谐波分解和自互补Top-Hat变换的轴承微弱故障特征提取方法。首先，通过改进广义谐波小波函数，使得信号分解后子带个数和带宽范围不受二进制分解方式的限制，并在此基础上应用谱峭度图方法确定信号中故障特征相对集中的最优频带；然后，对最优频带信号进行多尺度自互补Top-Hat变换，抑制背景噪声的干扰，突出微弱的故障冲击特征，并引入故障特征能量比的方法自适应确定最优结构元素的尺度；最后，通过包络解调提取出轴承微弱的故障特征。对仿真信号和实测轴承全寿命数据分析的结果表明，该方法能较为有效检测出轴承微弱的故障特征，具有较高的工程应用价值。

Weak Fault Feature Extraction Method of Bearing Based on Harmonic Decomposition and Self-complementary Top-Hat Transformation

Aiming at the problem that at early stage of bearing fault, the fault feature is very weak, feature components are easy to be submerged in background noise and can not be detected in time. This paper presents a novel method based on combination of harmonic decomposition and self-complementary Top-Hat transformation to enhance detection of bearing weak fault. Firstly, with the improved generalized harmonic wavelet function, the sub-band number and bandwidth range are not subject to the limitation of the binary decomposition, and the most optimal frequency band signal which contains relatively concentrated fault feature is found by kurtosis diagram of spectral kurtosis. Secondly, in order to depress strong background noise and enhance the weak fault feature of bearing, the optimum frequency band signal is handled by multi-scale self-complementary Top-Hat transformation, and the most optimal structure element (SE) scale is selected by using a novel method named fault feature energy radio (FFER). Finally, weak fault feature of bearing is detected by envelop demodulation analysis. The proposed method is applied to simulated signal and bearing full lifetime vibration datasets, the results show that this method can effectively detect weak fault of bearing, and is valuable for the engineering application.