A model-based approach to robot fault diagnosis

被引:0
|
作者
Liu, HH [1 ]
Coghill, GM [1 ]
机构
[1] Univ Aberdeen, Dept Comp Sci, Aberdeen AB24 3UE, Scotland
关键词
D O I
10.1007/1-84628-103-2_10
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a model-based approach to online robotic fault diagnosis: First Priority Diagnostic Engine (FPDE). The first principle of FPDE is that a robot is assumed to work well as long as its key variables axe within acceptable range. FPDE consists of four modules: the bounds generator, interval filter, component-based fault reasoning (core of FPDE) and fault reaction. The bounds generator calculates bounds of robot parameters based on interval computation and manufacturing standards. The interval filter provides characteristic values in each predetermined interval to denote corresponding faults. The core of FPDE carries out a two-stage diagnostic process: first it detects whether a robot is faulty by checking the relevant parameters of its end-effector, if a fault is detected it then narrows down the fault at component level. FPDE can identify single and multiple faults by the introduction of characteristic values. Fault reaction provides an interface to invoke emergency operation or tolerant control, even possibly system reconfiguration. The paper ends with a presentation of simulation results and discussion of a case study.
引用
收藏
页码:137 / 150
页数:14
相关论文
共 50 条
  • [31] Model-Based Fault Diagnosis Approach for Rotor System with Unidentified Supporting Parameters
    Yao, Hongliang
    Ma, Hongbin
    Han, Qingkai
    Wen, Bangchun
    [J]. MECHATRONICS AND INFORMATION TECHNOLOGY, PTS 1 AND 2, 2012, 2-3 : 773 - 778
  • [32] Model-based approach for fault diagnosis using set-membership formulation
    Chatti, Nizar
    Guyonneau, Remy
    Hardouin, Laurent
    Verron, Sylvain
    Lagrange, Sebastien
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2016, 55 : 307 - 319
  • [33] NONLINEAR FAULT DIAGNOSIS OF JET ENGINES BY USING A MULTIPLE MODEL-BASED APPROACH
    Naderi, E.
    Meskin, N.
    Khorasani, K.
    [J]. PROCEEDINGS OF THE ASME TURBO EXPO 2011, VOL 1, 2011, : 63 - 75
  • [34] A model-based approach to robot joint control
    Stronger, D
    Stone, P
    [J]. ROBOCUP 2004: ROBOT SOCCER WORLD CUP VIII, 2005, 3276 : 297 - 309
  • [35] An algebraic approach to model-based diagnosis
    Luan, Shangmin
    Magnani, Lorenzo
    Dai, Guozhong
    [J]. MODEL-BASED REASONING IN SCIENCE, TECHNOLOGY, AND MEDICINE, 2007, 64 : 467 - +
  • [36] MODEL-BASED APPROACH FOR FAULT-DIAGNOSIS .1. PRINCIPLES OF DEEP MODEL ALGORITHM
    CHANG, IC
    YU, CC
    LIOU, CT
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1994, 33 (06) : 1542 - 1555
  • [37] Structural abstraction for model-based diagnosis with a strong fault model
    Elimelech, Orel
    Stern, Roni
    Kalech, Meir
    [J]. KNOWLEDGE-BASED SYSTEMS, 2018, 161 : 357 - 374
  • [38] On-line Fault-diagnosis Study: Model-Based Fault Diagnosis for Ultracapcitors
    Li, Jingzhi
    Wang, Guohui
    Wu, Lifeng
    Li, Xiaojuan
    [J]. PROCEEDINGS OF 2014 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-2014 HUNAN), 2014, : 158 - 162
  • [39] An algorithm based on structural analysis for model-based fault diagnosis
    Gelso, Esteban R.
    Castillo, Sandra M.
    Armengol, Joaquim
    [J]. ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT, 2008, 184 : 138 - +
  • [40] MODEL-BASED SHAPE DESIGN AND FAULT DIAGNOSIS FOR MICROREACTORS
    Tonomura, Osamu
    [J]. ICNMM 2009, PTS A-B, 2009, : 1397 - 1402