Hierarchical Multilabel Segmentation for System Identification Using Historical Data

Abstract

Model predictive controllers (MPC) utilize a model of the process to optimize the future trajectory using an objective function to obtain a control move plan. Any new MPC implementation requires model identification. The quality of the identified model depends on the information content of the data. Performing step tests to obtain informative data is time-consuming and may not be economical. Since the process data are stored for long-term in industries, this data can be used for identification. But this historical data contain informative data scattered among regions of insignificant variation, long-term disturbance effects, process interruptions, etc. Informative data required for identification can be mined from historical data by using appropriate machine learning techniques. This paper focuses on generating high quality data segments from historical records that can be used for identification of reliable process models for use in any model-based controller such as MPC. An interval-halving-based hierar...