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

fYear

2006

Firstpage

126

Lastpage

131

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;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE-ICASE, 2006. International Joint Conference

Conference_Location

Busan

Print_ISBN

89-950038-4-7

Electronic_ISBN

89-950038-5-5

Type

conf

DOI

10.1109/SICE.2006.315386

Filename

4108809