Bakker, J., Pechenizkiy, M., Zliobaite, I., Ivannikov , A. and Karkkainen, T, 2009. Handling Outliers and Concept Drift in Online Mass Flow Prediction in CFB Boilers. In: Third International Workshop on Knowledge Discovery from Sensor Data (SensorKDD’09), 28 June - 1 July, 2009., Paris, France, pp. 13-22.
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Official URL: http://dl.acm.org/citation.cfm?id=1601972
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.
|Item Type:||Conference or Workshop Item (Paper)|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
|Group:||School of Design, Engineering & Computing > Smart Technology Research Centre|
|Deposited By:||Dr Indre Zliobaite LEFT|
|Deposited On:||25 Oct 2011 14:31|
|Last Modified:||07 Mar 2013 15:49|
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