Tolerance-Aware Contrastive Siamese Networks for Few-Shot and Open-Set Analog Circuit Fault Diagnosis

Fine-grained and open-set fault diagnosis of analog circuits poses two challenges that generic deep classifiers fail to address. First, under a closed-set assumption, models assign unseen fault types to known classes with unwarranted confidence, lacking any mechanism to reject out-of-distribution inputs. Second, component manufacturing tolerances induce structured intra-class variation that causes the frequency responses of distinct fault modes to overlap, severely degrading fine-grained discrimination. This paper proposes a Tolerance-Aware Contrastive Siamese Network (TCSN), a metric-learning framework that constructs a discriminative embedding space jointly addressing both issues. The core contribution is the Tolerance-Aware Contrastive Loss (TACL), comprising a tolerance term \( (\mathcal{L}_{\mathrm{tol}}) \) that suppresses tolerance-induced intra-class scatter across Monte Carlo realizations, and a fine-grained term \( (\mathcal{L}_{\mathrm{fg}}) \) that enforces separation of near-identical overlapping classes. For open-set rejection, an energy-based scoring mechanism maps prototype distances into in-/out-of-distribution scores, identifying unknown faults without retraining. The framework is validated on a Sallen-Key second-order Butterworth low-pass filter benchmark (10 known and 2 unknown fault classes). It attains an open-set AUROC of 0.9309 and an FPR@95%TPR of 0.2500; for closed-set classification it reaches 90% accuracy (macro-F1: 0.88), and it retains 83.0% accuracy under one-shot conditions (n = 1). Under corrupted inputs it degrades gracefully, outperforming all baselines at 10 dB SNR. Ablations confirm the necessity of each component: removing \( \mathcal{L}_{\mathrm{tol}} \) drops the open-set AUROC by 0.3793, removing \( \mathcal{L}_{\mathrm{fg}} \) reduces fine-grained accuracy by 11 percentage points, and replacing energy scoring with distance thresholding lowers the AUROC by 0.2683.