基于振动信号分析和支持向量机的滚动轴承故障诊断

针对滚动轴承出现故障时产生的振动信号具有非平稳信号的特点,通过小波包变换提取故障信号的特征向量,采用支持向量机分类器对提取的特征向量进行多类故障分类.通过与BP神经网络分类器进行对比研究,结果表明,在有限故障样本条件下,支持向量机分类器比BP神经网络分类器具更好的分类性能.

Fault Diagnosis of Rolling Element Bearing Based on Vibration Signal Analysis and Support Vector Machine

In order to deal with the non-stationary vibration signals generated by a fault in rolling bearing, some feature vectors from the fault signals by means of wavelet packet are extracted and the support vector machine (SVM) classification algorithm to the classification of faults in rolling bearing is applied. By drawing a comparison between the classification and BP neural network, the experiment shows that SVM algorithm has a better classification performance than BP neural network among limited fault samples.