Deconfounding enhanced image-based knowledge distillation for fault diagnosis with an application in manufacturing process

Deep learning (DL) models are widely adopted in fault diagnosis due to their powerful feature extraction capabilities, yet their high computational burden restricts deployment on edge devices. Knowledge distillation (KD) offers a lightweight solution by transferring knowledge from complex teacher models to lightweight student models. However, existing KD methods often fail to extract fault-specific features in images, as background features—highly correlated with labels—can mislead the model. To address this, we propose a deconfounding-enhanced knowledge distillation (DE-KD) method that integrates causal inference into KD to eliminate spurious correlations caused by background confounders. Specifically, a variational autoencoder (VAE) is incorporated to reconstruct the background of input images, enabling the teacher model to isolate and focus on fault-relevant features. The background reconstruction error is used to extract causal feature maps, which are then aligned with intermediate representations in the student model. The student model is trained using a multi-loss function incorporating hard labels, soft labels from the teacher, and deconfounded intermediate features. Applied to carbon accumulation diagnosis in automotive conductor rails, DE-KD achieveshigher accuracy (95.63 %)and improved interpretability compared to state-of-the-art lightweight methods, demonstrating its effectiveness in industrial scenarios.