Analysis of an Intelligent Hybrid System for Fault Diagnosis in Cracked Structure

被引:0
|
作者
Amiya Kumar Dash
Dayal R. Parhi
机构
[1] ITER,Department of Mechanical Engineering
[2] NIT,Department of Mechanical Engineering
关键词
GA; Fuzzy; Condition monitoring; Vibration; Multiple cracks;
D O I
暂无
中图分类号
学科分类号
摘要
To develop a robust fault diagnostic tool based on genetic algorithm and fuzzy logic, the current research explores the use of dynamic responses of cracked and intact cantilever beam structure. Theoretical, finite element and experimental analyses were carried out to find the combined impact of crack locations and crack depths on the vibrational characteristics (natural frequencies, mode shapes) of the cantilever beam. The calculated vibration signatures are used to design and train the GA-fuzzy controller. The inputs to the GA layer of the hybrid system are the first three relative natural frequencies and the first three relative mode shape differences. The interim outputs from the GA controller are the first three relative natural frequencies and the first three relative mode shape differences that are used as inputs to the fuzzy segment of the proposed hybrid technique. The final outputs from the developed hybrid system are rcl1_final, rcd1_final, rcl2_final and rcd2_final. The viability of the proposed technique has been investigated both analytically and experimentally for the cantilever beam containing multiple cracks. From the analysis of the results obtained from theoretical, finite element, GA-fuzzy controller and experimental analysis, the applicability of the proposed method for multiple cracks diagnosis is discussed.
引用
收藏
页码:1337 / 1357
页数:20
相关论文
共 50 条
  • [21] Structure and model of a fault intelligent diagnosis system for power station thermal systems
    Ma, Liangyu
    Duan, Wei
    Gao, Jianqiang
    Wang, Bingshu
    Tong, Zhensheng
    Dianli Xitong Zidonghue/Automation of Electric Power Systems, 2002, 26 (07): : 50 - 54
  • [22] A hybrid intelligent system for medical diagnosis
    Meesad, P
    Yen, GG
    IJCNN'01: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2001, : 2558 - 2563
  • [23] Industrial applications of the intelligent fault diagnosis system
    Jämsä-Jounela, SL
    Vermasvuori, M
    Haavisto, S
    Kämpe, J
    PROCEEDINGS OF THE 2001 AMERICAN CONTROL CONFERENCE, VOLS 1-6, 2001, : 4437 - 4442
  • [24] Study on integrated intelligent system for fault diagnosis
    Li, Y
    Yu, HQ
    Feng, ZS
    ISTM/2003: 5TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-6, CONFERENCE PROCEEDINGS, 2003, : 587 - 590
  • [25] A Fault Detecting System of Intelligent Detection and Diagnosis
    Shao, Renping
    Li, Yonglong
    Hu, Wentao
    ADVANCED MANUFACTURING SYSTEMS, PTS 1-3, 2011, 201-203 : 2300 - 2306
  • [26] An intelligent bearing fault diagnosis system: A review
    Saufi, S. R.
    Ahmad, Z. A. B.
    Leong, M. S.
    Lim, M. H.
    ENGINEERING APPLICATION OF ARTIFICIAL INTELLIGENCE CONFERENCE 2018 (EAAIC 2018), 2019, 255
  • [27] A new intelligent Hierarchical Fault Diagnosis System
    Huang, YC
    Yang, HT
    Huang, CL
    IEEE TRANSACTIONS ON POWER SYSTEMS, 1997, 12 (01) : 349 - 356
  • [28] New intelligent hierarchical fault diagnosis system
    Natl Cheng Kung Univ, Tainan, Taiwan
    IEEE Trans Power Syst, 1 (349-356):
  • [29] Fault diagnosis system based on intelligent agent
    Wu, Weiwei
    Yang, Shuzi
    Wu, Jinpei
    Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, 2000, 13 (01): : 78 - 82
  • [30] An intelligent online machine fault diagnosis system
    Fong, ACM
    Hui, SC
    COMPUTING & CONTROL ENGINEERING JOURNAL, 2001, 12 (05): : 217 - 223