Generative Few-Shot Siamese Networks for Anomaly Detection: Application to Pipeline Leakage in Nuclear Power Plants

In safety-critical industrial environments such as nuclear power plants (NPPs), early detection of pipeline leakage is essential for maintaining operational safety. However, leakage events are rare, abnormal samples are difficult to collect, and obtaining sufficient condition-specific normal data is also challenging. To address these limitations, this paper proposes SiameseGAD, a generative few-shot anomaly detection framework for pipeline leakage detection in the secondary systems of NPPs. The proposed method formulates leakage detection as a few-shot normality-modeling problem rather than as a problem of directly learning anomaly patterns. The Siamese network learns similarity relationships among normal samples and constructs a normal feature manifold, while anomaly scores are computed based on the distance from the estimated normal distribution. To improve normal distribution estimation under limited data, a denoising diffusion probabilistic model (DDPM) is used to generate in-distribution normal variants to augment the support-set. The main contribution of SiameseGAD lies in combining metric-learning-based few-shot normality modeling with normal-to-normal generative augmentation, enabling anomaly detection using only a few normal samples without relying on real anomaly data or synthetic anomaly generation. In the three evaluated target classes, SiameseGAD achieved an average AUROC of 93.59% and an average accuracy of 95.26%. These results indicate the potential of SiameseGAD for few-shot anomaly detection using only normal support samples, without requiring real or synthetically generated anomaly samples during inference.