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Robust fault detection and monitoring of hybrid process systems with uncertain mode transitions

Hu, Y.; El-Farra, N. H.

AIChE journal. VOL 57; NUMBER 10, ; 2011, 2783-2794 -- John Wiley & Sons, Ltd -- 2011

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  • Title:
    Robust fault detection and monitoring of hybrid process systems with uncertain mode transitions
  • Author: Hu, Y.;
    El-Farra, N. H.
  • Found In: AIChE journal. VOL 57; NUMBER 10, ; 2011, 2783-2794
  • Journal Title: AIChE journal.
  • Subjects: Chemical Engineering and Technology; Pharmaceutical Chemistry; Environment; LCC: TP1; Dewey: 660.2
  • Publication Details: John Wiley & Sons, Ltd
  • Language: English
  • Abstract: Abstract A methodology for fault detection and monitoring of a class of hybrid process systems modeled by switched nonlinear systems with control actuator faults, uncertain continuous dynamics, and uncertain mode transitions is presented. A robust hybrid monitoring scheme that distinguishes reliably between faults, mode transitions, and uncertainty is developed using tools from unknown input observer theory and results from Lyapunov stability theory. The monitoring scheme consists of (1) a family of dedicated mode observers that locate the active operating mode at any given time and detect mode switches, (2) a family of robust Lyapunov-based fault detection schemes that detect the faults within the continuous modes, and (3) a supervisor that synchronizes the switching between different controllers and different fault detectors as the process transitions from one mode to another. A key idea of the developed framework is to design the mode observers in a way that facilitates the identification of the active mode without information from the controllers and renders the residuals insensitive to the faults and uncertainties in the constituent subsystems. The implementation of the developed monitoring scheme is demonstrated using a simulated model of a chemical reactor that switches between multiple operating modes. 2010 American Institute of Chemical Engineers AIChE J, 2011
  • Identifier: Journal ISSN: 0001-1541
  • Publication Date: 2011
  • Accrual Information: Monthly
  • Shelfmark(s): 0773.071200
  • UIN: ETOCRN298077722

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