小波神经网络故障诊断系统的设计与应用

采用能量分布特征提取方法和优化BP算法，提出了一种基于小波变换和BP神经网络的故障诊断系统。利用该系统对汽车变速箱三挡齿轮磨损程度进行估计，诊断结果与实际完全吻合，表明该小波神经网络故障诊断系统的有效性。由于小波分析特别适用于非平稳信号的处理，因此该小波神经网络诊断系统对复杂机械设备的故障诊断有着广阔的应用前景。

Implementation and Application of a Neural Network Fault Diagnosis System Based on Wavelet Transform

Vibration signals of a machine are proved to be non stationary ones. They usually carry the dynamic information of a machine and are very useful for fault diagnosis. The wavelet analysis is especially suitable for a non stationary signal processing and the artificial neural network is a very good tool for signal identification. In this paper, a new efficient fault diagnosis system based on the wavelet transform and the artificial neural networks was presented, the theoretical background of wavelet transform was given, and a much better BP algorithm based on the traditional BP algorithm was introduced. Accordingly, a new wavelet neural networks fault diagnosis system was developed. The proposed fault diagnosis system was tested on a gear wear estimation of gearbox. The results showed that the developed system was effective.