Research on Feature Extraction Method for Operating Data of Nuclear Power Plant

被引：0
|
作者：
Peng B. [1 ]
Xia H. [1 ]
Zhu S. [1 ]
Peng M. [1 ]
Liu Y. [1 ]
Ma X. [1 ]
机构：
[1] Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, Harbin
来源：
| 1600年 / Atomic Energy Press卷 / 54期
关键词：
Artificial intelligence; Condition monitoring; Feature extraction; Nuclear power plant; Sparse autoencoder;
D O I：
10.7538/yzk.2019.youxian.0229
中图分类号：
学科分类号：
摘要：
Compared with the past, there are more important parameters available for collection and monitoring during the operation of nuclear power plant due to the deve-lopment of sensor technology. This situation not only increases the task of the operator, but also increases the load on the monitoring system. Since most parameters are correlated and some are redundant, the effective information in the parameters can be represented by a few parameters. In response to the above premise, the sparse autoencoder was used in this paper to extract the features of operating parameters of nuclear power plant. These feature data were then used in condition monitoring. The results show that using the data obtained by feature extraction for condition monitoring can not only improve the accuracy of state monitoring, but also reduce the computing resource, and this conclusion is applicable to both single and multiple normal working conditions. The results have important guiding significance for improving the safety of nuclear power plant. © 2020, Editorial Board of Atomic Energy Science and Technology. All right reserved.
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页码：488 / 495
页数：7
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