Optimized data acquisition by time series clustering in OPC

Huang, TH; Song, X; Huang, ML

Optimized data acquisition by time series clustering in OPC

Huang, TH Song, X Huang, ML

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011, 2011, pp. 2486 - 2492
Issue Date:: 2011-09-05

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Field	Value	Language
dc.contributor.author	Huang, TH	en_US
dc.contributor.author	Song, X	en_US
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.date.issued	2011-09-05	en_US
dc.identifier.citation	Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011, 2011, pp. 2486 - 2492	en_US
dc.identifier.isbn	9781424487554	en_US
dc.identifier.uri	http://hdl.handle.net/10453/19110
dc.description.abstract	How to optimize OPC Group Management is the most frequently asked question when integrating OPC with a SCADA system. Group management assumes that the OPC client has the information to partition OPC items into homogeneous OPC groups with optimal configuration parameters, such as update rate or deadband. In reality, supervised group management mandates an empirical configuration which often leads to high group polling rate on the server and low item update rate on the client. In this paper we propose an unsupervised OPC group management concept and algorithm by modeling the OPC items as time series functions in order to quantify the similarities. Partitioning items into the optimal OPC groups is achieved using the hierarchical clustering that does not require the number of optimal clusters to be known in advance as oppose to K-mean which often produces suboptimal result and reduce the homogeneity within the group. An evaluation comparison is provided for the unsupervised and supervised method that suggests that our approach produced outstanding performance. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011	en_US
dc.relation.isbasedon	10.1109/ICIEA.2011.5976011	en_US
dc.title	Optimized data acquisition by time series clustering in OPC	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Beijing, China	en_US
dc.location.activity	Auckland, NZ
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

How to optimize OPC Group Management is the most frequently asked question when integrating OPC with a SCADA system. Group management assumes that the OPC client has the information to partition OPC items into homogeneous OPC groups with optimal configuration parameters, such as update rate or deadband. In reality, supervised group management mandates an empirical configuration which often leads to high group polling rate on the server and low item update rate on the client. In this paper we propose an unsupervised OPC group management concept and algorithm by modeling the OPC items as time series functions in order to quantify the similarities. Partitioning items into the optimal OPC groups is achieved using the hierarchical clustering that does not require the number of optimal clusters to be known in advance as oppose to K-mean which often produces suboptimal result and reduce the homogeneity within the group. An evaluation comparison is provided for the unsupervised and supervised method that suggests that our approach produced outstanding performance. © 2011 IEEE.

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http://hdl.handle.net/10453/19110