Data reconciliation and gross-error detection for dynamic systems

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Abstract

Gross-error detection plays a vital role in parameter estimation and data reconciliation for dynamic and steady-state systems. Data errors due to miscalibrated or faulty sensors or just random events nonrepresentative of the underlying statistical distribution can induce heavy biases in parameter estimates and reconciled data. Robust estimators and exploratory statistical methods for the detection of gross errors as the data reconciliation is performed are discussed. These methods have the property insensitive to departures from ideal statistical distributions and to the presence of outliers. Once the regression is done, the outliers can be detected readily by using exploratory statistical techniques. Optimization algorithm and reconciled data offer the ability to classify variables according to their observability and redundancy properties. Here an observable variable is an unmeasured quantity that can be estimated from the measured variables through the physical model, while a nonredundant variable is a measured variable that cannot be estimated other than through its measurement. Variable classification can be used to help design instrumentation schemes. An efficient method for this classification of dynamic systems is developed. Variable classification and gross-error detection have important connections, and gross-error detection on nonredundant variables has to be performed with caution.

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