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

Authors:
J. Bakker;M. Pechenizkiy;I. Žliobaitė;A. Ivannikov;T. Kärkkäinen
Affiliations:
TU Eindhoven, The Netherlands;TU Eindhoven, The Netherlands;TU Eindhoven, The Netherlands;U. Jyväskylä, Finland;U. Jyväskylä, Finland
Venue:
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Year:
2009

Citing 6
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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.