DocumentCode :
486249
Title :
Post-Processing of Filter Residuals for Parametric Identification of Stochastic Model Faults
Author :
Whitney, David A.
Author_Institution :
The Analytic Sciences Corporation, Reading, Massachusetts 01867
fYear :
1985
fDate :
19-21 June 1985
Firstpage :
335
Lastpage :
340
Abstract :
This paper describes an application of state-space parametric identification techniques for determining the presence and nature of un-modeled stochastic components that are present in test data but not in the assumed (baseline) model (i.e., model faults). The technique uses residuals from a data processing filter based on the assumed model as inputs. This information is combined with information about the structure of the assumed model to build a second-stage identification filter which is used as the core of the parametric identification procedure. The approach uses the small-dimensioned second-stage filter to capture the disburbance (unmodeled stochastic components) dynamics and noise structure. The use of a small, independent second-stage filter, where alternative fault structures and locations can be experimented with and compared, allows the freedom to perform off-line model structure verification. Alternative model structures are evaluated statistically through the use of likelihood ratios and chi-square test statistics. Simulation results are given for a 76-state guidance system model.
Keywords :
Data processing; Fault detection; Fault diagnosis; Information filtering; Information filters; Instruments; Maximum likelihood estimation; Parameter estimation; Stochastic processes; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, 1985
Conference_Location :
Boston, MA, USA
Type :
conf
Filename :
4788629
Link To Document :
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