Abstract
In this paper we consider an application of data mining technology to the analysis of time series data from a pilot circulating fluidized bed (CFB) reactor. We focus on the problem of the online mass prediction in CFB boilers. We present a framework based on switching regression models depending on perceived changes in the data. We analyze three alternatives for change detection. Additionally, a noise canceling and a state determination and windowing mechanisms are used for improving the robustness of online prediction. We validate our ideas on real data collected from the pilot CFB boiler.
Original language | English |
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Title of host publication | Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data (KDD 2009, Paris, France, June 28, 2009) |
Editors | O.A. Omitaomu, A.R. Ganguly, J. Gama, R.R. Vatsavai, N.V. Chawla, M.M. Gaber |
Place of Publication | New York |
Publisher | Association for Computing Machinery, Inc. |
Pages | 13-22 |
ISBN (Print) | 978-1-60558-668-7 |
DOIs | |
Publication status | Published - 2009 |
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Dive into the research topics of 'Handling outliers and concept drift in online mass flow prediction in CFB boilers'. Together they form a unique fingerprint.Prizes
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Best Paper Award of SensorKDD 2009
Bakker, J. (Recipient), Pechenizkiy, M. (Recipient), Zliobaite, I. (Recipient), Ivannikov, A. (Recipient) & Kärkkainen, T. (Recipient), 2009
Prize: Other › Career, activity or publication related prizes (lifetime, best paper, poster etc.) › Scientific