小波包特征熵-神经网络在轴承故障诊断中的应用

提出了一种基于小波包特征熵-神经网络的轴承故障诊断新方法。首先对采集到的轴承的振动信号进行三层小波包分解,提取小波包特征熵,然后构造信号的小波包特征向量,并以此向量作为故障样本对三层BP神经网络进行训练,实现智能化故障诊断。仿真结果表明该方法有效可行。

Application of neural network based on wavelet packet-characteristic entropy in rolling bearing fault diagnosis

A new fault diagnosis method of vibrating of hearings was proposed on the basis of neural network based on wavelet packet-characteristic entropy(WP-CE).Firstly,three layers wavelet packet decomposition of the acquired vibrating signals of hearings was performed and the wavelet packet-characteristic entropy was extracted;then the eigenvector of wavelet packet of the vibrating signals was constructed,the three layers BP neural network were trained to implement the intelligent fault diagnosis by taking this eigenvector as fault sample.The simulation result from the proposed method is effective and feasible.