%A WU Jianping, JIANG Bin, LIU Jianwei %T Fault diagnosis of asynchronous motor based on wavelet packet entropy and wavelet neural network %0 Journal Article %D 2017 %J Journal of Shandong University(Engineering Science) %R 10.6040/j.issn.1672-3961.0.2017.179 %P 223-228 %V 47 %N 5 %U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1668.shtml} %8 2017-10-20 %X A method based on wavelet packet entropy and wavelet neural network was presented for asynchronous motor to realize fault diagnosis. The signal with faulty information was pretreated by wavelet packet, the wavelet packet energy spectrum entropy and coefficient entropy was extracted. The feature vector of information entropy was constructed. When the feature vector was input into the wavelet neural network, we trained it to detect and output the fault mode, so as to realize the fault diagnosis. This method had good adaptive resolution and fault tolerance, and it could avoid local minima and slow convergence effectively. The experiment results showed that this method could be used for fault diagnosis of induction motors, which was better than BP neural network model with the same parameters.