Study of Camshaft Grinders Faults Prediction Based on RBF Neural Network

被引:3
|
作者
Dong Ting [1 ]
Wang Hongjun [1 ]
Shi Lei [1 ]
机构
[1] BISTU, Sch Mech & Elect Engn, Beijing 100192, Peoples R China
关键词
camshaft production line; camshaft grinder; faults prediction model; RBF Neural Network;
D O I
10.4028/www.scientific.net/AMM.141.519
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Maintenance schemes in manufacturing systems are devised to reset the machines functionality in an economical fashion and keep it within acceptable levels. Camshaft grinders play the important role for the camshaft production line which is the massive production type. The camshaft grinders working condition is one of the critical sections which affected the production efficiency and profit of the manufactures. Nowadays the maintenance based on condition is carried out in order to meet the requirements of the market. The Time Between Failures (TBF) could be used for arranging the maintenance schedule. The faults prediction model based on RBF neural network, adopted K-means clustering algorithm to select clustering centre of radial basis function neural network (RBFNN), is proposed for the camshaft grinders which are the key equipment of camshaft production line. The TBF of the camshaft grinders are predicted by using this model, where the distribution density is 1, with the accepted network approximation error. An industrial example is used to illustrate the application of this model. The proposed method is effective and can be used for the suggestions for the practical workshop machines maintenance.
引用
收藏
页码:519 / 523
页数:5
相关论文
共 50 条
  • [31] Prediction algorithm of weld seam deviation based on RBF neural network
    [J]. Gao, X. (gaoxd666@126.com), 1600, Harbin Research Institute of Welding (33):
  • [32] Error Prediction method of Electronic Transformers Based on RBF Neural Network
    Wu Qiao
    Xu Xin
    Yang Shihai
    Lu Shufeng
    Chen Gang
    [J]. PROCEEDINGS OF 2017 13TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS (ICEMI), VOL 1, 2017, : 567 - 571
  • [33] Earthquake prediction by RBF neural network ensemble
    Liu, Y
    Wang, Y
    Li, Y
    Zhang, BF
    Wu, GF
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 2, 2004, 3174 : 962 - 969
  • [34] Temperature prediction of PV/T component based on RBF neural network
    Li, Jiyong
    Zhao, Zhendong
    Nan, Junpei
    Li, Yisheng
    Tang, Yunfeng
    [J]. PROCEEDINGS OF THE 2017 6TH INTERNATIONAL CONFERENCE ON ENERGY, ENVIRONMENT AND SUSTAINABLE DEVELOPMENT (ICEESD 2017), 2017, 129 : 469 - 473
  • [35] A Combinational QoS-Prediction Approach Based on RBF Neural Network
    Zhang, Pengcheng
    Sun, Yingtao
    Li, Wenrui
    Song, Wei
    Leung, Hareton
    [J]. PROCEEDINGS 2016 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC 2016), 2016, : 577 - 584
  • [36] Prediction of Spectrum based on Improved RBF Neural Network in Cognitive Radio
    Zhang, Shibing
    Hu, Jinming
    Bao, Zhihua
    Wu, Jianrong
    [J]. PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON WIRELESS INFORMATION NETWORKS AND SYSTEMS (WINSYS 2013), 2013, : 241 - 245
  • [37] Mechanical Property Parameters Prediction of Tube Based on RBF Neural Network
    Jia Meihui
    Tang Chengtong
    Liu Jianhua
    Zhang Tian
    [J]. MECHATRONICS AND APPLIED MECHANICS II, PTS 1 AND 2, 2013, 300-301 : 882 - 888
  • [38] Improved Prediction Method of Protein Contact Based on RBF Neural Network
    Sun Pengfei
    Zhang Jianpei
    [J]. 2009 3RD INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING, VOLS 1-11, 2009, : 816 - 819
  • [39] The Application of RBF Neural Network in Earthquake Prediction
    Wang Ying
    Chen Yi
    Zhang Jinkui
    [J]. THIRD INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING, 2009, : 465 - 468
  • [40] Stock price prediction by RBF neural network
    Huang, Guanghui
    Wa, Jianpin
    [J]. DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2007, 14 : 119 - 125