Fuzzy fault tree reliability analysis based on improved T-S model with application to NC turret

被引:3
|
作者
Wu, Yue [1 ,2 ]
Yang, Zhaojun [1 ,2 ]
Wang, Jili [1 ,2 ]
Hu, Wei [1 ,2 ]
Balakrishnan, N. [3 ]
机构
[1] Jilin Univ, Key Lab CNC Equipment Reliabil, Minist Educ, Changchun 130025, Peoples R China
[2] Jilin Univ, Sch Mech & Aerosp Engn, Changchun 130025, Peoples R China
[3] McMaster Univ, Dept Math & Stat, Hamilton, ON L8S 4K1, Canada
关键词
Fuzzy fault tree analysis; Takagi and Sugeno model; Monte Carlo simulation; Reliability; NC turret;
D O I
10.1007/s00170-021-08118-8
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In Takagi and Sugeno (T-S) fuzzy fault tree analysis (FFTA), the construction of T-S fuzzy gates relies too much on expert experience, which will result in inevitable subjective errors. In order to overcome this disadvantage, a new method was proposed in which the construction of T-S gates no longer relies solely on historical data and expert experience but is also determined by the importance of the basic events to the top event. In the proposed method, fault degrees were described as fuzzy numbers; fault probabilities were described as fuzzy possibilities. The importance index of basic events can be solved through the analysis of the fuzzy fault tree model by Monte Carlo (MC) simulation. The proposed method is suitable for systems where exact information on the fault probabilities of the components and the magnitude of failure and effect on the system are not available. The concept and calculation method of T-S probability importance was presented. Finally, the proposed method is applied to analyze the reliability of the NC turret seal subsystem, the accuracy of the method is verified by comparing with the methods based on traditional FFTA and T-S FFTA, and the weak points of the system are obtained by importance analysis, which will provide data for system fault diagnosis and preventive maintenance.
引用
收藏
页码:3837 / 3846
页数:10
相关论文
共 50 条
  • [41] A novel T-S fuzzy fault tree hybrid method for failure risk and multi-state reliability analysis of integrated production manufacturing system based on CPS
    Xiaoping Bai
    Xiangyun Gu
    Journal of Mechanical Science and Technology, 2023, 37 : 1819 - 1828
  • [42] T-S fuzzy model identification based on hyperplane
    School of Energy and Environment, Southeast University, Nanjing 210096, China
    不详
    Dianli Zidonghua Shebei Electr. Power Autom. Equip., 2008, 3 (14-17):
  • [43] Fuzzy predictive control based on T-S model
    Xing, Zong-Yi
    Hu, Wei-Li
    Jia, Li-Min
    Kongzhi yu Juece/Control and Decision, 2005, 20 (05): : 495 - 499
  • [44] Impulsive control based on the T-S fuzzy model
    Liu Guodong
    Li Yujan
    Li Nan
    DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES A-MATHEMATICAL ANALYSIS, 2006, 13 : 45 - 47
  • [45] Analysis and simulation on reliability based on fault tree model
    Jia, Yu-shuang
    Shi, Xian-ming
    Xiangtan Daxue Ziran Kexue Xuebao, 2001, 23 (04): : 101 - 105
  • [46] Fault diagnosis of hydraulic power system for coal mine tunnel drilling rig based on T-S fuzzy fault tree
    Liu R.
    Zhang Y.
    Yao K.
    Meitiandizhi Yu Kantan/Coal Geology and Exploration, 2022, 50 (12): : 194 - 202
  • [47] Dynamic Reliability Assessment Method for a Pantograph System Based on a Multistate T-S Fault Tree, Dynamic Bayesian
    Chen, Yafeng
    Wen, Jing
    Tian, Yingjie
    Zheng, Shubin
    Zhong, Qianwen
    Chai, Xiaodong
    APPLIED SCIENCES-BASEL, 2023, 13 (19):
  • [48] T-S fuzzy model identification and the fuzzy model based controller design
    Kung, Chung-Chun
    Su, Jui-Yiao
    2007 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-8, 2007, : 1904 - 1909
  • [49] T-S Fuzzy Model-based Fault Diagnosis of Pan Fuel Cell Engine
    Quan, Rui
    Quan, Shuhai
    Huang, Liang
    MECHANICAL ENGINEERING AND GREEN MANUFACTURING, PTS 1 AND 2, 2010, : 92 - +
  • [50] T-S Fuzzy Model-Based Fault Detection for Continuous Stirring Tank Reactor
    Wang, Yanqin
    Ren, Weijian
    Liu, Zhuoqun
    Li, Jing
    Zhang, Duo
    PROCESSES, 2021, 9 (12)