Data-Driven Soft Fault Diagnosis for Analog Circuits Based on Contrastive Learning

Analog circuits are fundamental to modern electrical systems, yet soft fault diagnosis of analog circuits remains challenging due to their nonlinear characteristics and the increasing scale of circuit integration. To address this issue, this article presents a novel data-driven fault diagnosis method utilizing contrastive learning (CL). In contrast to conventional data-driven approaches, the proposed method employs a self-supervised CL framework for representation learning, which captures informative features related to circuit faults. A temporal information-based encoder, leveraging 1-D convolutional neural networks (1D-CNNs) and a self-attention (SA) mechanism, is designed to enhance the extraction of latent patterns from raw time series data without relying on traditional signal processing techniques, thereby minimizing information loss and the need for expert knowledge. The encoder, trained using CL, is followed by a supervised two-layer neural network (NN) for fault classification. Simulation results on two benchmark filter circuits demonstrate the efficacy of the proposed method. Furthermore, the robustness of the encoder is validated under different faulty degrees, underscoring its reliability in various diagnostic scenarios.