Technology extraction from time series data reflecting expert operator skills and knowledge
Setsuya Kurahashi · International Journal of Computer Applications in Technology · 2008
Continuation processes in chemical and/or biotechnical plants always generate a large amount of time series data. However, since conventional process models are described as a set of control models, it is difficult to explain the complicated and active plant behaviours. Based on the background, this paper proposes a novel method to develop a process response model from continuous time-series data. The method consists of the following phases: (1) reciprocal correlation analysis, (2) process response model, (3) extraction of control rules, (4) extraction of a workflow and (5) detecting outliers. The main contribution of the research is to establish a method to mine a set of meaningful control rules from Learning Classifier System (LCS) using the Minimum Description Length (MDL) criteria and Tabu search method. The proposed method has been applied to an actual process of a biochemical plant and has shown the validity and the effectiveness.