Abstract: Deep learning is widely used in fault diagnosis, but currently, deep learning-based fault diagnosis models for analog circuits are relatively complex and difficult to deploy on edge devices. To address this issue and further improve fault diagnosis accuracy, a simple and lightweight deep learning model for analog circuit fault diagnosis, named Patches-CNN, is proposed. Firstly, the input image is divided into patches and transformed into word vectors (tokens) through a Patch Embedding operator, serving as the input for a ViT-style homogeneous structure. Feature extraction and information acquisition among tokens are carried out using the lightweight operator GSConv, which can effectively enhance the fault diagnosis accuracy of the model. Secondly, layer normalization is added to prevent gradient explosion and accelerate model convergence. To increase the nonlinearity of the model, the GELU activation function is employed. Finally, the Sallen-Key band-pass filter circuit and the Four-Opamp biquad high-pass filter circuit are used as experimental subjects. Experimental results demonstrate that this model can achieve accurate fault classification and location.

Key words: analog circuit, fault diagnosis, deep learning, homogeneous structure, layer normalization