滚动轴承特征提取的多分量解调方法

Multicomponent Demodulation Method for Rolling Bearing Fault Feature Extraction

提出利用多个高频振动分量进行滚动轴承故障特征提取的多分量解调方法。与传统的基于单一高频振动分量的解调方法不同,多分量解调方法从多个高频振动分量中提取信号特征信息。首先构建带通滤波器组对原信号进行滤波,然后依据所提高频振动分量获取策略求取原信号中多个高频振动分量,并对各高频振动分量进行包络检波,其次用独立成分分析对所得包络信号进行盲分离,最后对分离信号进行频谱变换以提取故障特征信息。仿真信号和故障轴承信号的分析结果表明,所提方法较传统解调方法更能凸显滚动轴承故障振动信号中的特征信息。

A new method based on multiple resonance components, named multicomponent demodulation method, is proposed for rolling bearing fault feature extraction. Different from the traditional demodulation method based on single resonance component, the proposed method extracts the fault features from multiple resonance components. Firstly, the designed band- pass filters are used to filter the original signal. Secondly, multiple resonance components are selected out by the proposed resonance component selection strategy, and each selected resonance component is demodulated by envelope detection. Thirdly, the independent component analysis is used to achieve the separation of the envelope signal. Fourthly, spectrum analysis is carried out to the separated signal to get the characteristic defect frequeney. The analysis results of the simulated signal and rolling bearing vibration signal with an inner race fault show that the proposed method is better able to extract the rolling bearing fault feature than traditional demodulation method.