Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

被引：0
|
作者：
宋羽 [1 ]
姜庆超 [1 ]
颜学峰 [1 ]
机构：
[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education(East China University of Science and Technology)
来源：
Journal of Central South University | 2015年 / 22卷 / 02期
基金：
中国国家自然科学基金; 中央高校基本科研业务费专项资金资助;
关键词：
statistic pattern framework; self-organizing map; fault diagnosis; process monitoring;
D O I：
暂无
中图分类号：
TP277 [监视、报警、故障诊断系统];
学科分类号：
0804 ; 080401 ; 080402 ;
摘要：
A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern(SP) framework integrated with a self-organizing map(SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman(TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes.Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.
引用
收藏
页码：601 / 609
页数：9
相关论文
共 50 条
  • [1] Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map
    Song Yu
    Jiang Qing-chao
    Yan Xue-feng
    [J]. JOURNAL OF CENTRAL SOUTH UNIVERSITY, 2015, 22 (02) : 601 - 609
  • [2] Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map
    Yu Song
    Qing-chao Jiang
    Xue-feng Yan
    [J]. Journal of Central South University, 2015, 22 : 601 - 609
  • [3] Using improved self-organizing map for fault diagnosis in chemical industry process
    Chen, Xinyi
    Yan, Xuefeng
    [J]. CHEMICAL ENGINEERING RESEARCH & DESIGN, 2012, 90 (12): : 2262 - 2277
  • [4] Process monitoring and modeling using the self-organizing map
    Alhoniemi, E
    Hollmén, J
    Simula, O
    Vesanto, J
    [J]. INTEGRATED COMPUTER-AIDED ENGINEERING, 1999, 6 (01) : 3 - 14
  • [5] Sound based induction motor fault diagnosis using Kohonen self-organizing map
    Germen, Emin
    Basaran, Murat
    Fidan, Mehmet
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2014, 46 (01) : 45 - 58
  • [6] Fault Diagnosis Method for Rectifier Circuit Based on Self-Organizing Map
    Shen, Runxia
    Lu, Yimin
    Liang, Qianqian
    [J]. MECHATRONICS AND INDUSTRIAL INFORMATICS, PTS 1-4, 2013, 321-324 : 1930 - 1933
  • [7] Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis
    Chen Xinyi
    Yan Xuefeng
    [J]. CHINESE JOURNAL OF CHEMICAL ENGINEERING, 2013, 21 (04) : 382 - 387
  • [8] Gearbox fault diagnosis using data fusion based on self-organizing map neural network
    Zhang Qiang
    Gu Jieying
    Liu Junming
    Tian Ying
    Zhang Shilei
    [J]. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2020, 16 (05)
  • [9] Risk-based fault detection using Self-Organizing Map
    Yu, Hongyang
    Khan, Faisal
    Garaniya, Vikram
    [J]. RELIABILITY ENGINEERING & SYSTEM SAFETY, 2015, 139 : 82 - 96
  • [10] Fault detection using hierarchical self-organizing map
    Ge, M
    Du, R
    Xu, YS
    [J]. 2003 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS, INTELLIGENT SYSTEMS AND SIGNAL PROCESSING, VOLS 1 AND 2, PROCEEDINGS, 2003, : 565 - 570
12345