基于形态非抽样小波分解的滚动轴承故障特征提取

Fault Feature Extraction of Rolling Element Bearing Based on Morphological Undecimated Wavelet Decomposition

针对滚动轴承故障特征信息往往被强背景噪声淹没的问题,提出采用基于多尺度差值形态滤波的形态非抽样小波分解方法提取故障特征。形态非抽样小波分解具有形态学的形态滤波特性与小波分解的多分辨率特性,通过非抽样方式对信号进行分解,克服了传统形态小波分解信息丢失的问题。结合差值形态滤波能够提取信号冲击成分的特点,构造了一种基于多尺度差值形态滤波的形态非抽样小波分解方法,并将其应用于滚动轴承故障特征的提取。仿真与实例证明,该方法可有效提取信号中的故障特征,比传统小波包分解效果更好。形态非抽样小波分解算法只包含加减和极大、极小运算,具有计算简单、快速等优点,适用于滚动轴承的在线监测与故障诊断。

Fault feature was always hidden by strong noise background in rolling element bearing fault signal.Based on morphological undecimated wavelet decomposition（MUWD）,a novel approach was proposed to extract rolling element bearing fault feature.MUWD possess both the characteristic of morphological filter in morphology and multiresolution in wavelet transform.Signal length was maintained invariable and information loss could be avoided in MUWD.Multi-scale MUWD was developed based on the characteristic of impulse feature extraction in difference morphological filter.The method was used to extract impulse feature in bearing fault signal.Experiment results showed that the presented method can achieve a better performance than traditional wavelet packet.MUWD algorithm includes addition,subtraction,maximum and minimum operations,and does not involve multiplication and division.It is suitable for on-line monitoring and fault diagnosis of bearing.