DocumentCode :
300537
Title :
Cluster analysis for multivariable process control
Author :
Sutanto, E. ; Warwick, K.
Author_Institution :
Dept. of Cybern., Reading Univ., UK
Volume :
1
fYear :
1995
fDate :
21-23 Jun 1995
Firstpage :
749
Abstract :
This paper describes the novel use of cluster analysis in the field of industrial process control. The severe multivariable process problems encountered in manufacturing have often led to machine shutdowns, where the need for corrective actions arises in order to resume operation. Production faults which are caused by processes running in less efficient regions may be prevented or diagnosed using reasoning based on cluster analysis. Indeed the internal complexity of a production machinery may be depicted in clusters of multidimensional data points which characterise the manufacturing process. The application of a mean-tracking cluster algorithm to field data acquired from a high-speed machinery is discussed. The objective of such an application is to illustrate how machine behaviour can be studied, in particular how regions of erroneous and stable running behaviour can be identified
Keywords :
multivariable control systems; pattern classification; process control; statistical analysis; cluster analysis; industrial process control; internal complexity; machine behaviour; mean-tracking cluster algorithm; multidimensional data points; multivariable process control; multivariable process problems; production faults; production machinery; Algorithm design and analysis; Clustering algorithms; Industrial control; Machinery; Manufacturing industries; Manufacturing processes; Multidimensional systems; Noise measurement; Process control; Production;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, Proceedings of the 1995
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2445-5
Type :
conf
DOI :
10.1109/ACC.1995.529350
Filename :
529350
Link To Document :
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