Handling outliers and concept drift in online mass flow prediction in CFB boilers

J. Bakker, M. Pechenizkiy, I. Zliobaite, A. Ivannikov, T. Kärkkäinen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

7 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the Third International Workshop on Knowledge Discovery from Sensor Data (KDD 2009, Paris, France, June 28, 2009)
EditorsO.A. Omitaomu, A.R. Ganguly, J. Gama, R.R. Vatsavai, N.V. Chawla, M.M. Gaber
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages13-22
ISBN (Print)978-1-60558-668-7
DOIs
Publication statusPublished - 2009

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    Bakker, J., Pechenizkiy, M., Zliobaite, I., Ivannikov, A., & Kärkkäinen, T. (2009). Handling outliers and concept drift in online mass flow prediction in CFB boilers. In O. A. Omitaomu, A. R. Ganguly, J. Gama, R. R. Vatsavai, N. V. Chawla, & M. M. Gaber (Eds.), Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data (KDD 2009, Paris, France, June 28, 2009) (pp. 13-22). Association for Computing Machinery, Inc. https://doi.org/10.1145/1601966.1601972