基于小波包变换与样本熵的滚动轴承故障诊断

针对滚动轴承振动信号的不规则性和复杂性可以反映轴承故障的发生和发展,提出一种基于小波包变换与样本熵的轴承故障诊断方法。样本熵可以较少地依赖时间序列的长度,将轴承振动信号进行3层小波包分解,利用分解得到的各个频带的样本熵值作为特征向量,利用支持向量机对轴承故障进行分类。对轴承内圈故障、滚动体故障和外圈故障3种故障及不同损伤程度的实测数据进行实验,结果表明该方法取得较高的识别率,具有一定的工程应用价值。

Roller Bearing Fault Diagnosis Based on Wavelet Packet Transform and Sample Entropy

According to the irregularity and complexity of roller bearing fault, and vibration signals can reflect the occurrence and development of the fault, a roller bearing fault diagnosis method based on wavelet packet transform (WPT) and Sample Entropy (SampEn) is proposed. SampEn is a measure that quantifies the complexity of a signal and has the advantage of being less dependent on time series length. The original bearing vibration signal is decomposed by wavelet packet transform. The sample entropy of the resultant wavelet packet coefficients are served as feature vector. In the classification, the support vector machine method is used to identify the different faults. Experiments are conducted on roller bearing with three different fault categories and several levels of fault severity. The experimental results indicate that the proposed approach could reliably identify the different fault categories. Thus, the proposed approach has possibility for bearing fault diagnosis.