Cointegration Testing Method for Monitoring Nonstationary Processes

被引:97
|
作者
Chen, Qian [1 ]
Kruger, Uwe [2 ]
Leung, Andrew Y. T. [3 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Aerosp Engn, Nanjing 210016, Peoples R China
[2] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland
[3] City Univ Hong Kong, Dept Bldg & Construct, Hong Kong, Hong Kong, Peoples R China
关键词
STATISTICAL PROCESS-CONTROL; DYNAMIC MULTIVARIATE PROCESSES; AUTOREGRESSIVE TIME-SERIES; FAULT-DIAGNOSIS; IMPACT;
D O I
10.1021/ie801611s
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
This paper introduces cointegration testing method for nonstationary process monitoring, which yields a long-run dynamic equilibrium relationship for nonstationary process systems. The process variables are examined, and then a cointegration model of the tested nonstationary variables is identified. The residual sequence of the cointegration model describes the dynamic equilibrium errors of the nonstationary process system and can be further analyzed for condition monitoring and fault detection purposes. The autocorrelated residual sequence is filtered with AR model first, then compensated to keep the fault signatures from being distorted by the filtering process. An application case study to an industrial distillation unit with a nonstatioanry process shows that a tidy cointegration model can describe the dynamic equilibruim state of the unit and correctly detect abnormal behavior of the process.
引用
收藏
页码:3533 / 3543
页数:11
相关论文
共 50 条
  • [1] Nonstationary system monitoring using cointegration testing method
    Xu, Zhen
    Chen, Qian
    [J]. DAMAGE ASSESSMENT OF STRUCTURES VII, 2007, 347 : 245 - +
  • [2] A Full-Condition Monitoring Method for Nonstationary Dynamic Chemical Processes with Cointegration and Slow Feature Analysis
    Zhao, Chunhui
    Huang, Biao
    [J]. AICHE JOURNAL, 2018, 64 (05) : 1662 - 1681
  • [3] Adaptive Testing for Cointegration With Nonstationary Volatility
    Boswijk, H. Peter
    Zu, Yang
    [J]. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2022, 40 (02) : 744 - 755
  • [4] Multiple Testing for No Cointegration under Nonstationary Volatility
    Demetrescu, Matei
    Hanck, Christoph
    [J]. OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 2018, 80 (03) : 485 - 513
  • [5] Total Variable Decomposition Based on Sparse Cointegration Analysis for Distributed Monitoring of Nonstationary Industrial Processes
    Zhao, Chunhui
    Sun, He
    Tian, Feng
    [J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2020, 28 (04) : 1542 - 1549
  • [6] Adaptive Cointegration Analysis and Modified RPCA With Continual Learning Ability for Monitoring Multimode Nonstationary Processes
    Zhang, Jingxin
    Zhou, Donghua
    Chen, Maoyin
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2023, 53 (08) : 4841 - 4854
  • [7] Recursive cointegration analytics for adaptive monitoring of nonstationary industrial processes with both static and dynamic variations
    Yu, Wanke
    Zhao, Chunhui
    Huang, Biao
    [J]. JOURNAL OF PROCESS CONTROL, 2020, 92 : 319 - 332
  • [8] Nonstationary Process Monitoring based on Cointegration Analysis with a Switching Scheme
    Jia, Chao
    An, Cheng
    Su, Wei
    [J]. 2020 CHINESE AUTOMATION CONGRESS (CAC 2020), 2020, : 6984 - 6988
  • [9] Nonstationary Process Monitoring Based on Cointegration Theory and Multiple Order Moments
    Wen, Jiatao
    Li, Yang
    Wang, Jingde
    Sun, Wei
    [J]. PROCESSES, 2022, 10 (01)
  • [10] Nonstationary Process Monitoring Based on Alternating Conditional Expectation and Cointegration Analysis
    Rao, Jingzhi
    Ji, Cheng
    Wen, Jiatao
    Wang, Jingde
    Sun, Wei
    [J]. PROCESSES, 2022, 10 (10)