Application of neural networks in fault diagnosis of rotating machinery

被引：0
|
作者：
Qing, He [1 ]
Dongmei, Du [1 ]
机构：
[1] North China Elect Power Univ, Coll Energy & Power Engn, Minist Educ, Key Lab Condit Monitoring & Control Power Plant E, Beijing 102206, Peoples R China
来源：
Proceedings of the ASME Power Conference 2007 | 2007年
关键词：
rotating machinery; vibration; fault diagnosis; neural networks;
D O I：
暂无
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：
The fault diagnosis method based on artificial neural networks is summarized. An object-oriented paradigm is introduced to fault diagnosis for large scale rotating machinery, for example, turbine-generator. A fault diagnosis method based on object-oriented artificial neural networks for more symptom domains is presented. The training patterns are constructed. A treatment for incomplete symptom domains and/or concurrent faults in diagnosing is given. Verification is carried out for the actual turbine-generator data with incomplete symptom domains.
引用
收藏
页码：279 / 282
页数：4
相关论文
共 50 条
  • [11] Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks
    Li, Hongmei
    Huang, Jinying
    Yang, Xiwang
    Luo, Jia
    Zhang, Lidong
    Pang, Yu
    [J]. ENTROPY, 2020, 22 (08)
  • [12] Ensemble adaptive convolutional neural networks with parameter transfer for rotating machinery fault diagnosis
    Ke Zhao
    Hongkai Jiang
    Xingqiu Li
    Ruixin Wang
    [J]. International Journal of Machine Learning and Cybernetics, 2021, 12 : 1483 - 1499
  • [13] Application of Deep Learning in Fault Diagnosis of Rotating Machinery
    Jiang, Wanlu
    Wang, Chenyang
    Zou, Jiayun
    Zhang, Shuqing
    [J]. PROCESSES, 2021, 9 (06)
  • [14] Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis
    Cai, Yuxiang
    Wang, Zhenya
    Yao, Ligang
    Lin, Tangxin
    Zhang, Jun
    [J]. Computational Intelligence and Neuroscience, 2022, 2022
  • [15] Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis
    Cai, Yuxiang
    Wang, Zhenya
    Yao, Ligang
    Lin, Tangxin
    Zhang, Jun
    [J]. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [16] ART Kohonen neural network for fault diagnosis of rotating machinery
    Yang, BS
    Han, T
    An, JL
    Kim, DJ
    [J]. ELEVENTH WORLD CONGRESS IN MECHANISM AND MACHINE SCIENCE, VOLS 1-5, PROCEEDINGS, 2004, : 2085 - 2090
  • [17] Fault diagnosis in rotating machinery
    Lees, AW
    [J]. IMAC-XVIII: A CONFERENCE ON STRUCTURAL DYNAMICS, VOLS 1 AND 2, PROCEEDINGS, 2000, 4062 : 313 - 319
  • [18] Rotating machinery fault diagnosis using a quadratic neural unit
    Rodriguez-Jorge, Ricardo
    Sanchez-Perez, Laura
    Bila, Jiri
    Skvor, Jiri
    [J]. INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING, 2022, 13 (2-3) : 309 - 319
  • [19] Fault identification in rotating machinery using artificial neural networks
    Nahvi, H
    Esfahanian, M
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2005, 219 (02) : 141 - 158
  • [20] Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms
    Sanz, Javier
    Perera, Ricardo
    Huerta, Consuelo
    [J]. JOURNAL OF SOUND AND VIBRATION, 2007, 302 (4-5) : 981 - 999
12345