Deep learning-based unsupervised representation clustering methodology for automatic nuclear reactor operating transient identification

Transient identification of condition monitoring data in nuclear reactor is important for system health assessment. Conventionally, the operating transients are correlated with the pre-designed ones by human operators during operations. However, due to necessary conservatism and significant differences between the operating and pre-designed transients, it has been less effective to manually identify transients, that usually contribute to different system degradation modes. This paper proposes a deep learning-based unsupervised representation clustering method for automatic transient pattern recognition based on the on-site condition monitoring data. Sample entropy is used as indicator for transient extraction, and a pre-training stage is implemented using an auto-encoder architecture for learning high-level features. An iterative representation clustering algorithm is further proposed to enhance the clustering effects, where a novel distance metric learning strategy is integrated. Experiments on a real-world nuclear reactor condition monitoring dataset validate the effectiveness and superiority of the proposed method, which provides a promising tool for transient identification in the real industrial scenarios. This study offers a new perspective in exploring unlabeled data with deep learning, and the end-to-end implementation scheme facilitates applications in the real nuclear industry. •A deep learning-based clustering method is proposed for automatic nuclear reactor operating transient identification.•An end-to-end transient identification framework is built, that requires little prior expertise.•A deep distance metric learning approach is proposed to enhance clustering effects.•Unlabeled data in nuclear industry can be effectively explored to guide engineers.•Experiments on a real-world nuclear reactor dataset validate the effectiveness of the proposed method.