Journal
JOURNAL OF PROCESS CONTROL
Volume 39, Issue -, Pages 21-34Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2015.12.004
Keywords
Data-driven modeling; Control performance monitoring; Contribution plot; Fault diagnosis; Industrial alarm system
Funding
- National Basic Research Program of China [2012CB720505]
- NSERC
- AITF
- National Natural Science Foundation of China [61433001]
- China Scholarship Council (CSC)
Ask authors/readers for more resources
Recently, slow feature analysis (SFA), a novel dimensionality reduction technique, has been adopted for integrated monitoring of operating condition and process dynamics. By isolating temporal behaviors from steady-state information, the SFA-based monitoring scheme enables improved discrimination of nominal operating point changes from real faults. In this study, we demonstrate that the temporal dynamics is an additional indicator of control performance changes, and further exploit its unique efficacy in control performance monitoring. Because of its data-driven nature and ease from first-principle knowledge, the SFA-based monitoring scheme allows an overall assessment of the plant-wide control performance and is compatible with different control strategies. An attractive feature of the SFA-based approach compared to existing ones is that generic process monitoring indices are used, which renders contribution plots naturally applicable to real-time diagnosis of control performance. As a result, potential fault variables as root causes of control performance changes can be identified, including not only controlled variables (CV) but also manipulated variables (MV) and disturbance variables (DV). Simulated and experimental studies demonstrate the effectiveness of the proposed method. (C) 2015 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available