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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Vydáno v:Journal of process control Ročník 12; číslo 2; s. 339 - 352
Hlavní autoři: Gatzke, Edward P., Doyle III, Francis J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.02.2002
Témata:
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
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Shrnutí: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