Self‐Supervised Anomaly Detection for Substation Equipment With Realistic Diffusion‐Based Synthesis and Adaptive Feature Refinement

Industrial anomaly detection is crucial for preventing equipment failures, yet challenges persist due to limited labelled data and complex fault patterns. This paper introduces the condition‐adaptive refinement (CARe) framework, a self‐supervised approach to anomaly detection that synthesises realistic training data through condition‐guided diffusion and adaptive feature refinement. The framework features three innovations: a condition‐controllable diffusion (CCD) model generates pseudo‐anomalous samples using spatial constraints, enhancing synthetic data. An adaptive feature refinement (AFR) module improves detection accuracy by reconstructing multi‐scale features. The method identifies anomalies by analysing reconstruction residuals without labelled data. Experiments validate the method's effectiveness, demonstrating substantial improvements in detection accuracy and generalisability. CARe offers a robust solution for industrial anomaly detection under data scarcity.