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