HU Feng, HUANG Liu-sheng, SUN Guo-ji. DETECTION AND IDENTIFICATION OF ABRUPT FAULTS FOR ARX PROCESS[J]. INFORMATION AND CONTROL, 2002, 31(3): 219-222,226.
Citation: HU Feng, HUANG Liu-sheng, SUN Guo-ji. DETECTION AND IDENTIFICATION OF ABRUPT FAULTS FOR ARX PROCESS[J]. INFORMATION AND CONTROL, 2002, 31(3): 219-222,226.

DETECTION AND IDENTIFICATION OF ABRUPT FAULTS FOR ARX PROCESS

More Information
  • Received Date: April 01, 2001
  • Published Date: June 19, 2002
  • Based on analyzing the Influence coming from pulse-type faults of inputs, two series of detection and identification algorithms, including off-line and on-line detection, are built. In order to build the on-line detection approaches, and deal with step-type faults, a simple transformation is suggested. Using this transformation, we can use all kinds of diagnosis methods to detect and identify step-type faults. At the end of this paper, a simulation is done to show that the on-line detection and identification algorithms are efficient and practical.
  • [1]
    王众托.系统工程引论.北京:电子工业出版社,1991
    [2]
    胡峰,孙国基.动态系统仿真建模技术综述(1,2).导弹试验技术,1998,1:1999,2
    [3]
    GE P Box,G M Jenkins.Time Series Analysis-Forecasting and Cibtrol,Holden-Day,San Francisco,1970
    [4]
    邓自立,郭一新.现代时间序列分析及其应用——建模、滤波、去卷、预报和控制.北京:知识出版社,1988
    [5]
    Hu Feng,Niu Hongli.Hierarchical Identification for Parameters of Autoregressive Models,Chinese Journal of Automation,New York:Allerton Press,1994,6(4):233~238
    [6]
    胡峰,范金城.动态-测量系统的M型滤波.智能控制与智能自动化(第三卷),1987~1991,科学出版社,1993
  • Related Articles

    [1]XIE Yanhong, LIU Wenjing, LI Yuan. Fault Detection in Multimode Industrial Processes Based on NNDSVDD[J]. INFORMATION AND CONTROL, 2018, 47(5): 541-546. DOI: 10.13976/j.cnki.xk.2018.7113
    [2]GUO Jinyu, ZHONG Lulu, LI Yuan. Fault Detection of Nonlinear Process Based on Differencial Locality Preserving Projections[J]. INFORMATION AND CONTROL, 2018, 47(2): 200-205, 213. DOI: 10.13976/j.cnki.xk.2018.0200
    [3]WU Dinghui, ZHAI Yanjie. Fault Diagnosis for the Pitch System of Wind Turbines Using the System Identification Algorithm[J]. INFORMATION AND CONTROL, 2016, 45(5): 563-567,574. DOI: 10.13976/j.cnki.xk.2016.0563
    [4]SHEN Yanxia, JI Lingyan, JI Zhicheng. Design of Fault-tolerant Controller for Wind Energy Conversion System Based on the RBF Neural Network Fault Observor[J]. INFORMATION AND CONTROL, 2015, 44(3): 359-366. DOI: 10.13976/j.cnki.xk.2015.0359
    [5]ZHANG Cheng, LI Yuan, GAO Xianwen. Fault-Detection Method for Batch Process Based on Sparse Distance[J]. INFORMATION AND CONTROL, 2014, 43(5): 588-595. DOI: 10.13976/j.cnki.xk.2014.0588
    [6]GUO Jinyu, CHEN Haibin, LI Yuan. kNN Fault Detection Method for Batch Process Based on Principal Sample Modeling Upgraded Online[J]. INFORMATION AND CONTROL, 2014, 43(4): 495-500. DOI: 10.13976/j.cnki.xk.2014.0495
    [7]ZHU Zijie, HUANG Xianghua. An Adaptive Neural Network Fault-Tolerant Control Using Backstepping[J]. INFORMATION AND CONTROL, 2010, 39(5): 531-535.
    [8]ZHAO Li-jie, WANG Gang, LI Yuan. STUDY OF A NONLINEAR PCA FAULT DETECTION AND DIAGNOSIS METHOD[J]. INFORMATION AND CONTROL, 2001, 30(4): 359-364.
    [9]GUO Qi-yi, CHEN Bang-xing, LU Gui-zhang, ZHAO Xin. DETECTION STRATEGY FOR FAULT DIAGNOSIS[J]. INFORMATION AND CONTROL, 2000, 29(3): 230-235.
    [10]LI Qingguo, FEMG Yuzhu, TONG Shaocheng, CHAI Tianyou. FAULT DETECTION AND FAULT TOLERANT CONTROL OF NONLINEAR SYSTEMS USING NEURAL NETWORKS[J]. INFORMATION AND CONTROL, 1998, 27(6): 440-445.

Catalog

    Article views (1153) PDF downloads (207) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return