Data reconciliation and gross error detection: A filtered measurement test

Measured process data commonly contain inaccuracies because the measurements are obtained using imperfect instruments. As well as random errors one can expect systematic bias caused by miscalibrated instruments or outliers caused by process peaks such as sudden power fluctuations. Data reconciliation is the adjustment of a set of process data based on a model of the process so that the derived estimates conform to natural laws. In this paper, we will explore a predictor-corrector filter based on data reconciliation, and then a modified version of the measurement test is combined with the studied filter to detect probable outliers that can affect process measurements. The strategy presented is tested using dynamic simulation of an inverted pendulum.