Deep Learning Analog Circuit Fault Diagnosis Based on Self-attention Mechanism
Abstract
solve the limitation of traditional diagnosis methods in complex fault identification. The research team designed an optimized deep learning
model that enhances multi-classification capabilities by introducing SoftMax layers, utilizes Dropout mechanism to mitigate overfitting, innovatively converts circuit signals into spectrograms as input, and combines location coding with class coding to improve the sequence processing capability of the self-attention mechanism. In the experiment, Sallen-Key low-pass filter was selected as the test object, and various data
sets including 24 fault types were generated by simulation software, which fully covered single fault and double fault cases. Transfer learning
strategy was adopted in model training, and the customized AlexNet network showed high accuracy in different fault classification. The results show that this method can effectively improve the accuracy and efficiency of fault diagnosis, and is of great significance to promote the
development of analog circuit fault detection technology.
Keywords
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DOI: http://dx.doi.org/10.70711/aitr.v2i4.4875
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