Enhanced neural network modelling for process fault diagnosis

被引：0
|
作者：
Chang, TK [1 ]
Yu, DL [1 ]
Williams, D [1 ]
机构：
[1] Liverpool John Moores Univ, Control Syst Res Grp, Liverpool L3 5UX, Merseyside, England
来源：
CONTROL APPLICATIONS IN MARINE SYSTEMS 2001 (CAMS 2001) | 2002年
关键词：
fault detection; fault isolation; neural networks; non-linear systems; process identification;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A neural network (NN) based fault detection and isolation (FDI) approach for unknown non-linear system is proposed. This FDI scheme is able to detect both actuator and sensor faults. An enhanced parallel (independent) NN model is trained to represent the process, and used to generate residual. A mean-weight strategy is introduced to overcome the unmodelled noise and disturbance problem. An information pre-processor is implemented to convert the quantitative residual to qualitative form and applied to a NN fault classifier to isolate different faults. The developed techniques are demonstrated with a multi-variable non-linear tank process. Copyright (C) 2001 IFAC.
引用
收藏
页码：215 / 220
页数：6
相关论文
共 50 条
  • [21] Fault Diagnosis of Vehicle Engine Based on Analytic Hierarchy Process and Neural Network
    Du, Chuanxiang
    Wei, Xiuling
    [J]. PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON MECHANICAL, ELECTRONIC, CONTROL AND AUTOMATION ENGINEERING (MECAE 2018), 2018, 149 : 143 - 150
  • [22] Application of fuzzy neural network to the nuclear power plant in process fault diagnosis
    Liu Yong-Kuo
    Xia Hong
    Xie Chun-li
    [J]. Journal of Marine Science and Application, 2005, 4 (1) : 34 - 38
  • [23] A hierarchical deep neural network for fault diagnosis on Tennessee-Eastman process
    Xie, Danfeng
    Bai, Li
    [J]. 2015 IEEE 14TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2015, : 745 - 748
  • [24] Fault Diagnosis of Chemical Process Based on ACO-BP Neural Network
    Chen Jian-xue
    [J]. ADVANCED MATERIALS AND PROCESS TECHNOLOGY, PTS 1-3, 2012, 217-219 : 2722 - 2725
  • [25] Adaptive neural network approach to on-line learning for process fault diagnosis
    Gomm, JB
    [J]. TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 1998, 20 (03) : 144 - 152
  • [26] Chemical process fault diagnosis based on mixup-convolution neural network
    Gu Xiaohua
    Song Hongfei
    Wang Tian
    Lu Fei
    Li Renjie
    [J]. PROCEEDINGS OF 2019 14TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS (ICEMI), 2019, : 1268 - 1274
  • [27] Enhanced Neural Network Modelling for a Real Multi-variable Chemical Process
    D. L. Yu
    J. B. Gomm
    [J]. Neural Computing & Applications, 2002, 10 : 289 - 299
  • [28] Computer network fault diagnosis based on neural network
    Zibo Vocational Institute, Zibo
    255314, China
    不详
    不详
    [J]. Int. J. Future Gener. Commun. Networking, 5 (39-50):
  • [29] Computer Network Fault Diagnosis Based On Neural Network
    Qian, Wang
    [J]. INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING, 2015, 8 (05): : 39 - 49
  • [30] Neural network modelling of a depollution process
    J. P. Steyer
    C. Pelayo-Ortiz
    V. González-Alvarez
    B. Bonnet
    A. Bories
    [J]. Bioprocess Engineering, 2000, 23 : 727 - 730
12345