Case-based Detection of Operating Conditions in Complex Nonlinear Systems

The case-based reasoning (CBR) system was developed for the identification of different operating situations in paper machines. Because similar break sensitivities can result from a multitude of dissimilar cases, the case base system is based on a division into categories which correspond to different levels of break sensitivity. The system is based on the linguistic equations (LE) approach and basic fuzzy logic, and it combines expert knowledge and on-line measurements from the wet-end of the paper machine. Nonlinear interactions are handled with a special scaling functions and linear equations. Each equation provides a new fact with a degree of membership, and the resulting set of facts is used in the fuzzy reasoning of process cases and break categories. The LE models are essential in compacting the system since each equation corresponds to a rule set in the fuzzy set systems. The application operates as a case retrieval and reuse application, predicting web break sensitivity in paper machine. Similarity measures based on model errors in the scaled range represent the importance of the models in activating the cases. The break category is defined by the case with the highest degree of membership. Although, the case base is fairly small the results from the on-line tests were relatively good compared to real break sensitivity. The predicted break sensitivity is an indirect measurement, which provides an early indication of process changes. The list of variables in the active cases can be used to avoid harmful operating conditions.