A fault diagnosis of nuclear power plant rotating machinery based on multi-sensor and deep residual neural network. (1st June 2023)

• Record Type: Journal Article
• Authors: Yin, Wenzhe; Xia, Hong; Wang, Zhichao; Yang, Bo; Zhang, Jiyu; Jiang, Yingying; Miyombo, Miyombo Ernest
• Is Part Of: Annals of nuclear energy. Volume 185(2023)
• Publication Date: 2023-06-01
• Subjects: Nuclear power plant -- Rotating machinery -- Fault diagnosis -- Deep residual neural network -- Multi-sensor; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2023.109700
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: A deep learning model based on multi-frequency domain information is proposed for rotating machinery diagnosis. The diagnostic framework has fast processing speed and does not require tedious parameter adjustment process. Generalization and anti-noise ability are improved by combining ResNet and multi-sensor data fusion. Abundant fault simulation experiments capture common rotating machinery failures to provide data support. Abstract: 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.