基于小波包和径向基神经网络轴承故障诊断

针对滚动轴承故障精密诊断的需要,采用小波包分析方法提取了滚动轴承故障的特征信号,通过小波包分析将高频信号分解到8个频带中,以频带能量作为识别故障的特征向量,应用RBF径向基神经网络建立了从特征向量到故障模式之间的映射,现场采集的数据分析表明,采用小波包和神经网络相结合的方法可以比较准确地识别滚动轴承的故障。

Accurate Diagnosis of Rolling Bearing Based on Wavelet Packet and RBF Neural Networks

The accurate diagnosis of rolling bearing was studied. The wavelet packet analysis was used to abstract the characteristic of signals. The signals were decomposed into eight frequency bands and the information in the high band was used as a characteristic vector. RBF neural networks were used to realize the map between the feature and diagnosis. The analysis of data sampled form a workshop testified correctness of the method proposed.