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Multivariable cluster analysis for high-speed industrial machinery

Sutanto, E. L. and Warwick, K. (1995) Multivariable cluster analysis for high-speed industrial machinery. IEE Proceedings - Science, Measurement and Technology, 142 (5). pp. 417-423. ISSN 1350-2344

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To link to this article DOI: 10.1049/ip-smt:19952161

Abstract/Summary

The overall operation and internal complexity of a particular production machinery can be depicted in terms of clusters of multidimensional points which describe the process states, the value in each point dimension representing a measured variable from the machinery. The paper describes a new cluster analysis technique for use with manufacturing processes, to illustrate how machine behaviour can be categorised and how regions of good and poor machine behaviour can be identified. The cluster algorithm presented is the novel mean-tracking algorithm, capable of locating N-dimensional clusters in a large data space in which a considerable amount of noise is present. Implementation of the algorithm on a real-world high-speed machinery application is described, with clusters being formed from machinery data to indicate machinery error regions and error-free regions. This analysis is seen to provide a promising step ahead in the field of multivariable control of manufacturing systems.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:17875
Uncontrolled Keywords:error-free regions, high-speed industrial machinery, internal complexity, machine behaviour, machinery error regions, mean-tracking algorithm, multidimensional points, multivariable cluster analysis, multivariable control, production machinery
Publisher:IET

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