Fisher information matrix based time-series segmentation of process data

Advanced chemical process engineering tools, like model predictive control or soft sensor solutions require proper process models. Parameter identification of these models needs input-output data with high information content. When model based optimal experimental design techniqes cannot be applied, the extraction of informative segements from historical data can also support system identification. We developed a goal-oriented Fisher information based time-series segmentation algorithm, aimed at selecting informative segments from historical process data. The utilized standard bottom-up algorithm is widely used in off-line analysis of process data. Different segments can support the identification of parameter sets. Hence, instead of using either D- or E-optimality as the criterion for comparing the information content of two input sequences (neigbouring segments), we propose the use of Krzanowski's similarity coefficient between the eigenvectors of the Fisher information matrices obtained from the sequences. The efficiency of the proposed methodology is demonstrated by two application examples. The algorithm is capable to extract segments with parameter-set specific information content from historical process data.