A fault diagnosis of nuclear power plant rotating machinery based on multi-sensor and deep residual neural network

被引：15

作者：

Yin, Wenzhe ^{[1
,2
]}

Xia, Hong ^{[1
,2
]}

Wang, Zhichao ^{[1
,2
]}

Yang, Bo ^{[1
,2
]}

Zhang, Jiyu ^{[1
,2
]}

Jiang, Yingying ^{[1
,2
]}

Miyombo, Miyombo Ernest ^{[2
,3
]}

机构：

[1] Harbin Engn Univ, Key Lab Nucl Safety & Adv Nucl Energy Technol, Minist Ind & Informat Technol, Harbin, Peoples R China

[2] Harbin Engn Univ, Fundamental Sci Nucl Safety & Simulat Technol Lab, Harbin, Peoples R China

[3] Radiat Protect Author, Explorat House, Ridgeway, POB 50002, Exploration House, Lusaka 50002, Zambia

来源：

ANNALS OF NUCLEAR ENERGY | 2023年 / 185卷

基金：

中国国家自然科学基金;

关键词：

Nuclear power plant; Rotating machinery; Fault diagnosis; Deep residual neural network; Multi-sensor;

D O I：

10.1016/j.anucene.2023.109700

中图分类号：

TL [原子能技术]; O571 [原子核物理学];

学科分类号：

0827 ; 082701 ;

摘要：

Rotating machinery is a key component of nuclear power plants (NPPs). The integrity of rotating machine is related to the safety and economy of the entire NPPs. In order to achieve better and more robust diagnostic performance, this work proposes an intelligent fault diagnosis method based on multi-sensor and deep residual neural network. This method gives full play to the value of multi-sensor information, and utilizes the powerful learning ability of deep learning model to realize the identification of rotating machinery fault types. The effectiveness of the method is evaluated by using the motor dataset and the bearing dataset from fault simulation experiment bench. In addition, the anti-noise ability of the method is tested and compared with other methods. The results show that the proposed method has higher diagnosis accuracy and stronger robustness, demonstrating the potential application value in rotating machinery of NPPs.

引用

页数：11

共 29 条

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