Based On Wavelet Packet And Rbf Network Diagnostic Techniques

The focus of research of intelligent fault diagnosis techniques is wavelet transform and neural networks. Some technical problems about wavelet transform and intelligent fault diagnosis techniques based on neural networks are discussed in the paper. The main contents of the paper are as follows:The key to training of a radial basis function (RBF) network is to determine the parameters of hidden layers of the network. There are a number of training methods of RBF networks. But the shortcomings of the methods are that the training speeds are too slow and the ability to classify is unstable. A new, improved training method of RBF networks, the method of Output-Input Cluster is presented in the paper. The method of Output-Input Cluster is based on the theory of minimization of the partitioning entropy. The sample data of a rotary machine indicate that the training time by using the method is shorter; the network is simpler; the results are more accurate and the influence of random noise is less than that by using other methods. And the method has good application foreground in fault diagnosis fields.The selection of mother functions of wavelet transform and wavelet packet transform and their bases is also discussed in the paper. There are vast application of wavelet transform and wavelet packet transform in the fault diagnosis fields. The transforms help us to obtain a number of feature information of fault signals. But in the face of a lot of mother function of wavelet transform and a number of bases after transform, we must select a proper mother function of wavelet transform and his bases, because not all mother functions and their bases are proper. In the fault diagnosis field, with theory of information entropy, a new method of selecting mother function and his bases is presented by using RBF networks. It helps us to determine...