Title :
Extracting Technology and Detecting Outliers from Process Time Series Data Reflecting Expert Operator Skills
Author :
Kurahashi, Setsuya ; Inagaki, Fumitatsu
Author_Institution :
Graduate Sch. of Bus. Sci., Tsukuba Univ., Tokyo
Abstract :
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; (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 using the minimum description length 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
Keywords :
biochemistry; biotechnology; control engineering computing; data mining; industrial plants; learning (artificial intelligence); multiskilling; pattern classification; process control; search problems; time series; biochemical plant; classifier system; continuous time-series data; control rule extraction; expert operator skills; process response model; process time series data; reciprocal correlation analysis; tabu search method; workflow extraction; Automatic control; Control systems; Data mining; Electronic mail; Manufacturing; Phase detection; Poles and towers; Process control; Quality control; Search methods; Data mining; Learning Classifier Systems; Minimum Description Length; Process Control; Tabu search;
Conference_Titel :
SICE-ICASE, 2006. International Joint Conference
Conference_Location :
Busan
Print_ISBN :
89-950038-4-7
Electronic_ISBN :
89-950038-5-5
DOI :
10.1109/SICE.2006.315386
