基于小波包能量谱与主成分分析的轴承故障特征增强诊断方法

滚动轴承出现损伤时，采集的振动信号呈非平稳性，采用一般的时域和频域分析方法不能准确提取出振动信号的故障特征。根据小波包多分辨、精细化的分解特性，提出一种基于小波包能量谱与主成分分析（PCA）方法的滚动轴承故障诊断算法。将振动信号进行小波包分解，得到重点频率段信息的能量谱，提取能量谱作为特征向量；利用PCA方法对特征向量降维并减小噪声信号的干扰，获得增强的故障特征；利用层次聚类方法和改进的模糊c均值聚类算法对不同类型的滚动轴承故障进行识别，两种聚类方法都准确地识别出了不同的故障类型。实例验证结果表明，所提方法能够有效地提取振动信号中的有用故障特征，实现轴承故障类型的精确诊断。

Fault Feature Enhancement Method for Rolling Bearing Fault Diagnosis Based on Wavelet Packet Energy Spectrum and Principal Component Analysis

The acquired vibration signal is usually unstable once rolling bearing damage occurs, which results in inaccurately detecting the fault features of rolling bearing by time-domain or frequency-domain analysis. A fault diagnosis method which uses the wavelet packet energy spectrum and principal component analysis (PCA) to diagnose the faults of rolling bearing is presented. The wavelet packet decomposition algorithm is used to decompose and refine the vibration signals in different frequency ranges. The energy spectra in the focused frequency ranges are calculated after the vibration signal is decomposed by wavelet packet decomposition. PCA is performed to decrease the dimension of the energy spectrum and reduce the noise interference, thus enhancing the extracted fault feature without the noise interference. And then the different fault types of rolling bearing are classified by two types of clustering algorithms, i.e., hierarchical clustering analysis (HCA) and fuzzy c-means (FCM). The results show that the fault types can be correctly identified by both cluster algorithms. The example verification indicates that the proposed method can be used to effectively extract the useful fault features in the vibration signal and identify the fault types exactly. This provides a feasible method for diagnosing a machine with some similar faults. Key