全矢改进连续谐波小波包滚动轴承故障特征提取

Fault Feature Extraction of Rolling Bearings Based on Full Vector Improved Continuous Harmonic Wavelet Packet

滚动轴承在旋转类机械设备中运行时,会产生成分复杂的振动信号。现有滚动轴承信号处理方法多使用单通道信息,无法反映整个截面故障状态。本文提出了一种基于全矢改进连续谐波小波包变换的故障特征提取方法。首先使用相互正交的两个传感器,实现滚动轴承某一截面上双通道振动信号采集;其次利用全矢谱技术将所采集的同源双通道信号进行融合;然后使用改进连续谐波小波包变换分解融合后的信号;再从各子带中提取能反映各类故障特征的能量值组成特征向量;最后利用美国凯斯西储大学滚动轴承实验台的一组实测故障数据验证该方法的正确性。

When rolling bearings operate in rotating mechanical equipment,complex vibration signals will be measured,which can reflect equipment′s operation condition.The existing signal processing methods of rolling bearings mostly use single channel information,which can not reflect the fault state of the whole section.In this paper,a fault feature extraction method based on full vector improved continuous harmonic wavelet packet transform is proposed.Firstly,two orthogonal sensors are used to realize the dual channel vibration signal acquisition on a certain section of rolling bearing.Secondly,the collected homologous dual channel signals are fused with full vector spectrum technology.Then,the fused signal is decomposed by improved continuous harmonic wavelet packet transform.Next,the energy values that can reflect the characteristics of various faults are extracted from each subband to form a feature vector.Finally,a group of measured fault data from the rolling bearing test-bed of Case Western Reserve University in the United States are used to verify the correctness of the method.