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Simplified time series representations for efficient analysis of industrial process data

Published online by Cambridge University Press:  07 November 2003

ESA ALHONIEMI
ESA ALHONIEMI
Affiliation:
Department of Information Technology, University of Turku, Turku, Finland
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Abstract

The data storage capacities of modern process automation systems have grown rapidly. Nowadays, the systems are able to frequently carry out even hundreds of measurements in parallel and store them in databases. However, these data are still rarely used in the analysis of processes. In this article, preparation of the raw data for further analysis is considered using feature extraction from signals by piecewise linear modeling. Prior to modeling, a preprocessing phase that removes some artifacts from the data is suggested. Because optimal models are computationally infeasible, fast heuristic algorithms must be utilized. Outlines for the optimal and some fast heuristic algorithms with modifications required by the preprocessing are given. In order to illustrate utilization of the features, a process diagnostics framework is presented. Among a large number of signals, the procedure finds the ones that best explain the observed short-term fluctuations in one signal. In the experiments, the piecewise linear modeling algorithms are compared using a massive data set from an operational paper machine. The use of piecewise linear representations in the analysis of changes in one real process measurement signal is demonstrated.

Keywords

Piecewise Linear ModelingPreprocessingProcess AnalysisProcess DataTime Series
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 2 , May 2003 , pp. 103 - 114
Copyright
2003 Cambridge University Press

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