The fault diagnosis research based on SOM-BP composite neural network learning algorithm

被引:2
|
作者
Song Yu [1 ]
Wang Fengxia [1 ]
Yi Lu [1 ]
机构
[1] North China Elect Power Univ, Coll Control & Comp Engn, Baoding, Peoples R China
关键词
composite neural network; fault diagnosis; simulation and test;
D O I
10.1109/ICCECT.2012.103
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Introduce the basic principle and learning algorithm of the SOM network and BP network. The diagnosis mode is established with the common breakdown condition and the related parameters of the gear boxes used as the training sample. Due to the complex nonlinear relation between breakdown mode and characteristic parameters of gear-boxes, the SOM-BP composite neural net work is used. First have a preliminary pattern recognition classification for training samples by SOM network and details of fault classification by BP network under the MATLAB 7.1 environment, through the simulation test and comparison with BP network, reliability of the composite neural network for gear box failure diagnosis are verified.
引用
收藏
页码:535 / 539
页数:5
相关论文
共 50 条
  • [1] A Kind of Fault Diagnosis Research Based on Improved SOM-BP Composite Neural Network
    Wu Kehe
    Huang Zhengguan
    Wang Zhao
    Hu Xin
    [J]. 2014 5TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS), 2014, : 465 - 467
  • [2] Fault diagnosis of shield machine based on SOM-BP neural network fusion
    Zou, Lan
    Liang, Li
    [J]. 2018 INTERNATIONAL CONFERENCE ON SENSING, DIAGNOSTICS, PROGNOSTICS, AND CONTROL (SDPC), 2018, : 232 - 237
  • [3] Application Study on SOM-BP Neural Network for Blower Fan Fault Diagnosis
    Ma, Fengying
    Wei, Tongfa
    [J]. 2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 5929 - 5934
  • [4] Fault Diagnosis of Levitation Controller of Medium-Speed Maglev Train Based on SOM-BP Neural Network
    Zhang, Xiaowen
    Xu, Jie
    Zhang, Hui
    [J]. 2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 4162 - 4167
  • [5] Research on Degradation State of Turnout Equipment Based on SOM-BP Hybrid Neural Network
    Gao, Limin
    Xu, Qingyang
    Li, Feng
    Yang, Ji
    Meng, Jinghui
    Yang, Shuzhong
    [J]. Zhongguo Tiedao Kexue/China Railway Science, 2020, 41 (03): : 50 - 58
  • [6] Application and research of the train fault diagnosis based on improved BP neural network algorithm
    Qu, Yingwei
    Yan, Yinnan
    Zheng, Guanghai
    [J]. 2012 INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING AND COMMUNICATION TECHNOLOGY (ICCECT 2012), 2012, : 43 - 47
  • [7] Research on pump fault diagnosis based on pso-bp neural network algorithm
    Sang, Jinguo
    [J]. PROCEEDINGS OF 2019 IEEE 8TH JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC 2019), 2019, : 1748 - 1752
  • [8] Research on bearing fault diagnosis based on improved genetic algorithm and BP neural network
    Chen, Zenghua
    Zhu, Lingjian
    Lu, He
    Chen, Shichao
    Zhu, Fenghua
    Liu, Sheng
    Han, Yunjun
    Xiong, Gang
    [J]. SCIENTIFIC REPORTS, 2024, 14 (01):
  • [9] Wind power interval prediction utilizing SOM-BP artificial neural network
    Kun, Ren
    Jihong, Qu
    [J]. Journal of Chemical and Pharmaceutical Research, 2014, 6 (04) : 858 - 863
  • [10] Fault Diagnosis and Application Based on SOM Neural Network
    Zhang, Hang
    Liu, Wencheng
    Wu, Yongjian
    Liu, Sheng
    [J]. 2ND INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND INDUSTRIAL AUTOMATION (ICITIA 2017), 2017, : 42 - 49