A new method that researching fault diagnosis of high-voltage (HV) circuit breaker (CB) is proposed. The
method combines Wavelet Packet (WP) with Radical Basis Function (RBF) Neural Network (NN). Firstly, by applying
the theory of WP decomposition and reconstruction, the mechanical vibration signal of CB was decomposed into different
frequency bands, and the coefficients are reconstructed in the corresponding node. After that, the feature vector was extracted
by equal-energy segment entropy from reconstructed signals. Finally, fault diagnosis has been realized through the
classification of feature parameters combined with RBF neural network. The experiment outputs show that the method
can be applied in diagnosis.