期刊
KNOWLEDGE-BASED SYSTEMS
卷 204, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2020.106178
关键词
Deep learning; Nuclear reactor; Clustering; Transient identification; Unsupervised learning
资金
- Nuclear Power Institute of China [N2005010, N180703018, N180708009, N170308028]
- Fundamental Research Funds for the Central Universities [11902202]
- National Natural Science Foundation of China
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. (C) 2020 Elsevier B.V. All rights reserved.
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