Analog Circuit Fault Depth Feature Extraction Method Based On Optimal Wavelet Packet

Due to the inherent characteristics of analog circuit,such as its nonlinearity,continuous response and tolerance on component parameters,etc,inducing the diversity and complexity of fault types of the circuit,it make difficult for analog circuit fault extract.The analog circuit is more and more complex,it is difficult for the conventional analog circuit fault extract theories and methods to achieve the expected results in practical engineering.Hence,it is very important to explore some efficient analog circuit fault feature extraction theories and fault feature optimization method to meet the development of analog circuit.Wavelet packet transform is a time-frequency analysis method,which is a common analog circuit fault feature extraction method.However,The use of wavelet packet transform to simulate the fault characteristics of the analog circuit to select different mother wavelet function will have different result of extraction;In order to obtain the optimal simulation circuit fault characteristics,this paper combines the energy and entropy to propose the wavelet function optimization algorithm,and at the same time,through the simulation example,the method proposed in this paper is valid.According to the characteristics of analog circuit fault feature,using deep learning fault feature to extract the fault features of the existing study,optimized to better reflect the fault data of analog circuit fault.The method is compared with the common method of fault diagnosis,fault diagnosis examples show that,deep learning can to effectively improve the accuracy of analog circuit fault diagnosis.