Dynamic Distributed Monitoring Strategy for Large-Scale Nonstationary Processes Subject to Frequently Varying Conditions Under Closed-Loop Control

Large-scale processes under closed-loop control are commonly subjected to frequently varying conditions due to load changes or other causes, resulting in typical nonstationary characteristics. For closed-loop control processes, the normal changes in operation conditions may distort the static and dy...

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Vydáno v:IEEE transactions on industrial electronics (1982) Ročník 66; číslo 6; s. 4749 - 4758
Hlavní autoři: Zhao, Chunhui, Sun, He
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Bayes methods
Bayesian analysis
Closed-loop control
dynamic distributed monitoring
frequent varying conditions
Heuristic algorithms
large-scale nonstationary processes
Monitoring
Principal component analysis
Process control
Process variables
Statistical inference
Steady-state
ISSN:0278-0046, 1557-9948
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Shrnutí:Large-scale processes under closed-loop control are commonly subjected to frequently varying conditions due to load changes or other causes, resulting in typical nonstationary characteristics. For closed-loop control processes, the normal changes in operation conditions may distort the static and dynamic variations in a different way from real faults. In this paper, a dynamic distributed monitoring strategy is proposed to separate the dynamic variations from the steady states, and concurrently, monitor them to distinguish changes in the normal operating condition and real faults for large-scale nonstationary processes under closed-loop control. First, large-scale nonstationary process variables are decomposed into different blocks to mine the local information. Second, the static and dynamic equilibrium relations are separated by probing into the cointegration analysis solution in each block. Third, the concurrent monitoring models are constructed to supervise both the steady variations and their dynamic counterparts for each block. Finally, the local monitoring results are combined by Bayesian inference to obtain global results, which enable description and monitoring of both static and dynamic equilibrium relations from the global and local viewpoints. The feasibility and performance of the proposed method are illustrated with a real industrial process, which is a 1000-MW ultra-supercritical thermal power unit.
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ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2018.2864703