Application of RBF neural network in fault diagnosis of ship analog circuits
A fault diagnosis method for ship analog circuits based on RBF neural network is proposed to address the complex interaction of components in ship analog circuits,the difficulty of highlighting fault signals in a large number of normal signals,and the difficulty of extracting and identifying fault features.The fault feature extraction method for ship simulation circuits based on wavelet packets captures the energy variation characteristics of the circuit frequency band through wavelet decomposition and reconstruction;Using state transition algorithm to optimize the fault diagnosis model of RBF neural network,optimizing the parameters of RBF neural network,constructing an RBF neural network model for diagnosing circuit faults,learning the relationship between extracted fault features and types,and diagnosing the new input ship analog circuit output signal fault type.The experimental test results show that after effectively capturing the energy changes in the frequency band of ship analog circuit faults,this method has not shown significant errors in the diagnosis of various ship analog circuit faults.
RBF neural networkship analog circuitfault diagnosisstate transition algorithm
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