Optimized data acquisition by time series clustering in OPC

IEEE Press
Publication Type:
Conference Proceeding
Proc. Of 6th IEEE Int'l Conference on Industrial Electronics and Applications (ICIEA 11), 2011, pp. 2480 - 2486
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How to optimized OPE Group Management is the most frequently asked question when integrating ope with a SeADA system. Group management assumes that the ope client has the information to partition ope items into homogeneous ope groups with optimal configuration parameters, such as update rate or dead band. 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 ope group management concept and algorithm by modeling the ope items as time series functions in order to quantify the similarities. Partitioning items into the optimal ope 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.
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