Graph of a Process-A New Tool for Finding Model Structures in a Model-Based Diagnosis

被引:17
作者
Sztyber, Anna [1 ]
Ostasz, Andrzej [2 ]
Koscielny, Jan M. [1 ]
机构
[1] Warsaw Univ Technol, Inst Automat Control & Robot, Faulty Mechatron, PL-02525 Warsaw, Poland
[2] GE Oil & Gas Warsaw, PL-00113 Warsaw, Poland
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2015年 / 45卷 / 07期
关键词
Causal graphs; fault diagnosis; model structure (MS); qualitative modeling; redundancy; SIGNED DIRECTED GRAPH; DIAGNOSABILITY ANALYSIS; FAULT-DIAGNOSIS; SYSTEM FAILURES; BOND GRAPHS; ALGORITHM; FRAMEWORK;
D O I
10.1109/TSMC.2014.2384000
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 this paper, graph of a process (GP) is introduced as a new formalization of causal graph useful in fault diagnosis. Faults are directly incorporated into the model. In this paper, we propose the concept of model structure (MS), which can be used for building fuzzy and neural models for fault detection. Algorithms for finding all MSs and methods for determining faults-symptoms relation are developed. This method is applicable to the design of model-based diagnostic systems, when only basic knowledge of the process to be diagnosed is available. No mathematical model is needed. This paper shows that GP can be constructed on the basis of process diagrams and expert knowledge. Models for fault detection can be built automatically from process measurements. Ideas are explained on a three-tank system example. In the last section, our proposed method is compared in detail with other existing approaches using qualitative models.
引用
收藏
页码:1004 / 1017
页数:14
相关论文
共 50 条
  • [31] OpenErrorPro: A New Tool for Stochastic Model-based Reliability and Resilience Analysis
    Morozov, Andrey
    Ding, Kai
    Steurer, Mikael
    Janschek, Klaus
    2019 IEEE 30TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE), 2019, : 303 - 312
  • [32] A New Model-Based Technique for the Diagnosis of Rotor Faults in RFOC Induction Motor Drives
    Cruz, Sgio M. A.
    Stefani, Andrea
    Filippetti, Fiorenzo
    Cardoso, Antnio J. Marques
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2008, 55 (12) : 4218 - 4228
  • [33] Model-Based Condition Monitoring of Modular Process Plants
    Wetterich, Philipp
    Kuhr, Maximilian M. G.
    Pelz, Peter F.
    PROCESSES, 2023, 11 (09)
  • [34] Hidden Markov model-based process monitoring system
    Xu, YS
    Ge, M
    JOURNAL OF INTELLIGENT MANUFACTURING, 2004, 15 (03) : 337 - 350
  • [35] Hidden Markov model-based process monitoring system
    Yangsheng Xu
    Ming Ge
    Journal of Intelligent Manufacturing, 2004, 15 : 337 - 350
  • [36] A model-based calibration approach for structural fault diagnosis using piezoelectric impedance measurements and a finite element model
    Ezzat, Ahmed Aziz
    Tang, Jiong
    Ding, Yu
    STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL, 2020, 19 (06): : 1839 - 1855
  • [37] Bond Graph Model-Based for IDA-PBC
    Garcia-Tenorio, Camilo
    Quijano, Nicanor
    Mojica-Nava, Eduardo
    Sofrony, Jorge
    2016 IEEE CONFERENCE ON CONTROL APPLICATIONS (CCA), 2016,
  • [38] Model-based fault detection and diagnosis of complex chemical processes: A case study of the Tennessee Eastman process
    Tidriri, Khaoula
    Chatti, Nizar
    Verron, Sylvain
    Tiplica, Teodor
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2018, 232 (06) : 742 - 760
  • [39] Model-Based Diagnosis of Multi-Agent Systems: A Survey
    Kalech, Meir
    Natan, Avraham
    THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2022, : 12334 - 12341
  • [40] Active Model-Based Fault Diagnosis in Reconfigurable Battery Systems
    Schmid, Michael
    Gebauer, Emanuel
    Hanzl, Christian
    Endisch, Christian
    IEEE TRANSACTIONS ON POWER ELECTRONICS, 2021, 36 (03) : 2584 - 2597