Analogue circuit fault diagnosis based on convolution neural network

By Tao Du, Hao Zhang, Ling Wang. 2019

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In order to simplify the process of analogue circuit fault diagnosis under the premise of improving the fault diagnosis rate of analogue circuit, and to deeply mine the fault characteristics of the output signal, a fault diagnosis method based on convolutional neural network (CNN) is proposed. The output signals in different fault states are directly input into CNN for fault feature extraction and fault classification. By optimising the CNN model and its parameters, the 100% fault diagnosis rate of Sallen-Key circuit can be achieved. The experimental results indicate that the CNN-based analogue circuit fault diagnosis method simplifies the fault diagnosis process and improves the fault diagnosis rate.

Subfields: Control and Systems Engineering