Research On Fault Diagnosis Method Of Analog Circuit Based On Deep Learning

With the continuous development of modern integrated electronic circuit technology,the scale of electronic circuits is rapidly growing.The integration and complexity of circuits is getting higher and higher,and the probability of failure of electronic circuit systems is also increasing.The failure of any component may cause the whole system to deviate from the normal state.In large-scale electronic circuits,the analog circuit part is most prone to failure.Due to the nonlinearity of the circuit and the tolerance of components,it is difficult to model the fault diagnosis of analog circuits.Studying how to use modern diagnostic techniques to accurately diagnose the location and components in which faults occur from large-scale electronic circuits has become an urgent problem in practical engineering.The main research contents of this paper are as follows:First of all,we study and analyze the advantages and disadvantages of the existing analog circuit fault data preprocessing technology.Most of the existing analog circuit fault data preprocessing techniques only use the time domain information of the circuit fault response and ignore the frequency domain information.A two-dimensional fault diagnosis model is proposed.and the fault diagnosis model is established by using the feature extraction ability of the deep learning method.The one-dimensional time domain signal is used to construct the time-frequency domain two-dimensional fault data model,and the raw data is processed into a data pattern that can be classified by convolutional neural network.Secondly,the overall scheme design of the analog circuit fault diagnosis system is designed.The faults in the typical analog circuit are designed.We study the method of obtaining fault data by simulation and the technology of storing massive data through the database system.Then,the two-dimensional processing method of fault data is studied.Finally,the deep learning network is used to train and test the fault data after processing.Then,we simulate the typical circuits used for analog circuit fault diagnosis,including shallen-key band-pass filter circuit,four-opamp biquad high-pass filter circuit,two-stage four-opamp low-pass filter circuit and wide-band high-accuracy amplification to obtain fault data,and then store the fault data into a database to realize unified management of the data.Finally,build a system test platform,and write database storage software and deep learning training test program,using MATLAB software as a convolutional neural network operation platform.Diagnose the correct rate of each fault by separately diagnosing the corresponding single fault and multiple faults of each fault circuit.By comparing the diagnostic accuracy of other fault diagnosis methods,the data is visualized to verify the operability and reliability of the model and system.