Abstract：A method that effectively de-noising vibration signal and extracting fault feature is necessary when diagnose the switching synchronism fault of low voltage circuit breaker(LVCB). A vibration signal analysis method based on Hilbert-Huang transform(HHT) is put forward,in which intrinsic mode function(IMF) components are extracted by empirical mode decomposition(EMD) to reflect local characteristic of LVCB vibration signal,and top five of IMF components energy not only is representation of vibration feature,but also has de-noising work. By analyzing to LVCB vibration signal in time-domain,kurtosis and mean square value are used as auxiliary mechanic characteristic index. With feature vector of energy ratio of front five IMF components,kurtosis and mean square value,the neural network based on particle swarm optimization(PSO) and radial basis function(RBF) is expounded to model fault recognition of asynchronous switching for LVCB. By results of experiment and simulation,it is effective to analysis switching synchronism with intelligent technology of comprehensive such as time-domain analyze,EMD and PSO-RBF neural network,which is a new diagnosis method for circuit breaker in special for three phases switching synchronism vibration of LVCB.
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2014, Vol. 29 
