Research on Fault Diagnosis of Nuclear Power System Based on Improved Linear Learning Algorithm

被引：0

作者：

Zhao X. ^{[1
]}

Cai Q. ^{[1
]}

Zhao X. ^{[1
]}

Wang X. ^{[1
]}

机构：

[1] Department of Nuclear Science and Engineering, Naval University of Engineering, Wuhan

来源：

Cai, Qi (13871162138@139.com) | 1600年 / Atomic Energy Press卷 / 41期

关键词：

Fault diagnosis framework; Improved linear learning; Marine nuclear power system; Support vector machine (SVM);

D O I：

10.13832/j.jnpe.2020.01.0134

中图分类号：

学科分类号：

摘要：

Because the types of nuclear power system accidents are various and the severity of accidents is difficult to determine, the hierarchical structure and nested structure are introduced on the basis of traditional linear model. The support vector machine classification model is selected as the diagnosis model in the structure, and the linear learning merges the results. By analyzing the operation process and mechanism of the accident, the effective identification area and sensitive parameters of the corresponding type of accident are determined. The results show that the final recognition accuracy rate is more than 99%, and it can provide reference for accident diagnosis in large-scale systems. © 2020, Editorial Board of Journal of Nuclear Power Engineering. All right reserved.

引用

页码：134 / 139

页数：5