Fault diagnosis of analog electronic circuits based on wavelet transform and neural network
Circuit fault is an important factor affecting the experimental progress and effect of analog electronic circuits,and in order to realize the rapid and accurate classification and diagnosis of faults,a fault diagnosis method based on wavelet transform and neural network is proposed.Firstly,the experimental process and circuit characteristics of analog electronic circuits are analyzed,and a feature extraction method combining wavelet decomposition and cross wavelet is proposed taking the circuit excitation and response signals as the starting point,and the feature vectors containing the signal frequency domain features and phase difference information are obtained.Then,a neural network algorithm based on L-M optimization is designed,and the feature vectors after dimensionality reduction and regularization are trained as the input parameters of the model,and the final diagnostic results are obtained.Finally,the test results on the fault set of the common emitter amplification circuits show that the proposed method can effectively improve the speed and accuracy of fault diagnosis of different circuits.
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