Study of diagnosis for rotating machinery in advanced nuclear reactor based on deep learning model

被引:2
|
作者
Sun, Yuanli [1 ]
Wang, Hang [2 ]
机构
[1] Tsinghua Univ, Nucl Res Inst, Beijing, Peoples R China
[2] Harbin Engn Univ, Nucl Sci & Technol, Harbin, Peoples R China
关键词
fault diagnosis model; deep learning; rotating machine; advanced nuclear reactor; improved transformer model; FAULT-DIAGNOSIS;
D O I
10.3389/fenrg.2023.1210703
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Many types of rotating mechanical equipment, such as the primary pump, turbine, and fans, are key components of fourth-generation (Gen IV) advanced reactors. Given that these machines operate in challenging environments with high temperatures and liquid metal corrosion, accurate problem identification and health management are essential for keeping these machines in good working order. This study proposes a deep learning (DL)-based intelligent diagnosis model for the rotating machinery used in fast reactors. The diagnosis model is tested by identifying the faults of bearings and gears. Normalization, augmentation, and splitting of data are applied to prepare the datasets for classification of faults. Multiple diagnosis models containing the multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN), and residual network (RESNET) are compared and investigated with the Case Western Reserve University datasets. An improved Transformer model is proposed, and an enhanced embeddings generator is designed to combine the strengths of the CNN and transformer. The effects of the size of the training samples and the domain of data preprocessing, such as the time domain, frequency domain, time-frequency domain, and wavelet domain, are investigated, and it is found that the time-frequency domain is most effective, and the improved Transformer model is appropriate for the fault diagnosis of rotating mechanical equipment. Because of the low probability of the occurrence of a fault, the imbalanced learning method should be improved in future studies.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Application of Rotating Machinery Fault Diagnosis Based on Deep Learning
    Cui, Wei
    Meng, Guoying
    Wang, Aiming
    Zhang, Xinge
    Ding, Jun
    SHOCK AND VIBRATION, 2021, 2021
  • [2] Research on Fault Diagnosis Method of Rotating Machinery Based on Deep Learning
    Chen, Zhouliang
    Li, Zhinong
    2017 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-HARBIN), 2017, : 1015 - +
  • [3] Translation Invariance-Based Deep Learning for Rotating Machinery Diagnosis
    Du, Wenliao
    Wang, Shuangyuan
    Gong, Xiaoyun
    Wang, Hongchao
    Yao, Xingyan
    Pecht, Michael
    SHOCK AND VIBRATION, 2020, 2020
  • [4] Application of Deep Learning in Fault Diagnosis of Rotating Machinery
    Jiang, Wanlu
    Wang, Chenyang
    Zou, Jiayun
    Zhang, Shuqing
    PROCESSES, 2021, 9 (06)
  • [5] Development of deep reinforcement learning-based fault diagnosis method for rotating machinery in nuclear power plants
    Qian, Gensheng
    Liu, Jingquan
    PROGRESS IN NUCLEAR ENERGY, 2022, 152
  • [6] A fault diagnosis method for nuclear power plants rotating machinery based on deep learning under imbalanced samples
    Yin, Wenzhe
    Xia, Hong
    Huang, Xueying
    Wang, Zhichao
    ANNALS OF NUCLEAR ENERGY, 2024, 199
  • [7] A review on deep learning based condition monitoring and fault diagnosis of rotating machinery
    Gangsar P.
    Bajpei A.R.
    Porwal R.
    Noise and Vibration Worldwide, 2022, 53 (11): : 550 - 578
  • [8] A Physics-based Deep Learning Approach for Fault Diagnosis of Rotating Machinery
    Sadoughi, Mohammadkazem
    Hu, Chao
    IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, 2018, : 5919 - 5923
  • [9] A CAE-Based Deep Learning Methodology for Rotating Machinery Fault Diagnosis
    Yang, Daoguang
    Sun, Kangkang
    2021 7TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND ROBOTICS (ICCAR), 2021, : 393 - 396
  • [10] Robust Deep Learning-Based Diagnosis of Mixed Faults in Rotating Machinery
    Chen, Siyuan
    Meng, Yuquan
    Tang, Haichuan
    Tian, Yin
    He, Niao
    Shao, Chenhui
    IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2020, 25 (05) : 2167 - 2176