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.

核反应堆状态监测数据的瞬态识别对于系统健康评估非常重要。通常，操作人员在操作过程中将操作瞬变与预先设计的瞬变相关联。但是，由于必要的保守性以及操作和预先设计的瞬态之间的显着差异，手动识别瞬态的效果较差，这通常会导致不同的系统降级模式。提出了一种基于现场状态监测数据的基于深度学习的无监督表示聚类方法，用于瞬时模式自动识别。样本熵用作瞬态提取的指标，并且使用自动编码器体系结构实施预训练阶段以学习高级特征。进一步提出了一种迭代表示聚类算法，以增强聚类效果，其中集成了一种新颖的距离度量学习策略。在真实世界的核反应堆状态监测数据集上进行的实验验证了该方法的有效性和优越性，这为在实际工业场景中进行瞬态识别提供了有希望的工具。这项研究为通过深度学习探索未标记的数据提供了新的视角，并且端到端实施方案促进了在实际核工业中的应用。这为在实际工业场景中进行瞬态识别提供了有希望的工具。这项研究为通过深度学习探索未标记的数据提供了新的视角，并且端到端实施方案促进了在实际核工业中的应用。这为在实际工业场景中进行瞬态识别提供了有希望的工具。这项研究为通过深度学习探索未标记的数据提供了新的视角，并且端到端实施方案促进了在实际核工业中的应用。