Multi-Scale Descriptions of Real-Time Trends and their Impact in Pattern Recognition

Recognition and interpretation of real-time process trends is the center-piece of the socalled intelligent controller, which recognizes faults, performance degradation, and scopes the problems associated with efficient disturbance rejection and process model/controller adaptation. In this paper we will show how a unifying symbolic representation of process trends leads to a systematic extraction of their temporal features and precise classification of trends. Such classification may proceed in a syntactic, semi-quantitative, or fully quantitative manner, depending on the characteristics of the problem being solved. Furthermore, we will argue that a multi-scale representation of process trends is essential in pattern recognition, and we will show how the unifying representation of trends allows for the decomposition of signals at various levels of abstraction.