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.
|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|
|Publication status||Published - 2009|
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