Use of multiple models and qualitative knowledge for on-line moving horizon disturbance estimation and fault diagnosis

An integrated fault detection, fault isolation, and parameter estimation technique is presented in this paper. Process model parameters are treated as disturbances that dynamically affect the process outputs. A moving horizon estimation technique minimizes the error between process and model measure...

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Bibliographic Details
Published in:	Journal of process control Vol. 12; no. 2; pp. 339 - 352
Main Authors:	Gatzke, Edward P., Doyle III, Francis J.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.02.2002
Subjects:	Disturbance estimation Mixed integer programming Moving horizon estimation Multiple linear models Disturbance estimation Mixed integer programming Multiple linear models Moving horizon estimation
ISSN:	0959-1524, 1873-2771
Online Access:	Get full text
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Summary:	An integrated fault detection, fault isolation, and parameter estimation technique is presented in this paper. Process model parameters are treated as disturbances that dynamically affect the process outputs. A moving horizon estimation technique minimizes the error between process and model measurements over a finite horizon by calculating model parameter values across the estimation horizon. To implement qualitative process knowledge, this minimization is constrained such that only a limited number of different faults (parameters) may change during a specific horizon window. Multiple linear models are used to capture nonlinear process characteristics such as asymmetric response, variable dynamics, and changing gains. Problems of solution multiplicity and computational time are addressed. Results from a nonlinear chemical reactor simulation are presented.
ISSN:	0959-1524 1873-2771
DOI:	10.1016/S0959-1524(01)00037-3