Convergent Cross-mapping based Fault Detection and Diagnosis for Non-linear Dynamic Systems

被引:0
|
作者
Sharma, Shivom [1 ]
Lakshminarayanan, S. [1 ]
Karimi, I. A. [1 ]
Srinivasan, R. [2 ]
机构
[1] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 117585, Singapore
[2] Indian Inst Technol Madras, Dept Chem Engn, Chennai 600036, India
关键词
Convergent Cross-mapping; Fault Detection; Fault Diagnosis; Fault Propagation; Causality Detection; PRINCIPAL COMPONENT ANALYSIS;
D O I
暂无
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 study, the convergent cross-mapping technique has been applied to non-linear dynamic systems for operation monitoring, detecting process abnormalities or faults, and identifying their root cause(s). Faults in sensor or controller are easier to detect and diagnose compared to process faults (e.g., change in composition of feed). Therefore, we have focused on sensor and process faults for two non-linear dynamic systems. Our fault detection and root cause diagnosis methodology uses convergent cross-mapping along with some standard statistical techniques. Moreover, causal link lists are deduced to visualize the propagation of a fault - the correctness of these data generated causal link lists have also been validated with existing process knowledge.
引用
收藏
页码:299 / 304
页数:6
相关论文
共 50 条
  • [1] Fuzzy observers for non-linear dynamic systems fault diagnosis
    Patton, RJ
    Chen, J
    Lopez-Toribio, CJ
    PROCEEDINGS OF THE 37TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-4, 1998, : 84 - 89
  • [2] Overview of Fault Detection and Identification for Non-linear Dynamic Systems
    Zhou, Yimin
    Xu, Guoqing
    Zhang, Qi
    2014 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA), 2014, : 1040 - 1045
  • [3] Fault diagnosis for multivariable non-linear systems based on non-linear spectrum feature
    Zhang, Jialiang
    Cao, Jianfu
    Gao, Feng
    TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL, 2017, 39 (07) : 1017 - 1026
  • [4] Genetic programming based approaches to identification and fault diagnosis of non-linear dynamic systems
    Witczak, M
    Obuchowicz, A
    Korbicz, J
    INTERNATIONAL JOURNAL OF CONTROL, 2002, 75 (13) : 1012 - 1031
  • [5] Fault detection and diagnosis for non-linear processes empowered by dynamic neural networks
    Gravanis, Georgios
    Dragogias, Ioannis
    Papakiriakos, Konstantinos
    Ziogou, Chrysovalantou
    Diamantaras, Konstantinos
    COMPUTERS & CHEMICAL ENGINEERING, 2022, 156
  • [6] Dynamic causality analysis using overlapped sliding windows based on the extended convergent cross-mapping
    Ge, Xinlei
    Lin, Aijing
    NONLINEAR DYNAMICS, 2021, 104 (02) : 1753 - 1765
  • [7] Dynamic causality analysis using overlapped sliding windows based on the extended convergent cross-mapping
    Xinlei Ge
    Aijing Lin
    Nonlinear Dynamics, 2021, 104 : 1753 - 1765
  • [8] Non-linear dynamic systems fault detection and isolation using fuzzy observers
    Chen, J
    Lopez-Toribio, CJ
    Patton, RJ
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 1999, 213 (I6) : 467 - 476
  • [9] Structural approach to fault diagnosis in non-linear systems
    Zhukova, S.
    Zhirabok, A.
    Annals of DAAAM for 2005 & Proceedings of the 16th International DAAAM Symposium: INTELLIGENT MANUFACTURING & AUTOMATION: FOCUS ON YOUNG RESEARCHES AND SCIENTISTS, 2005, : 397 - 398
  • [10] Linearization techniques in fault diagnosis of non-linear systems
    Lin, W
    Wang, H
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND CONTROL ENGINEERING, 2000, 214 (I4) : 241 - 245