research-article

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

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T. KärkkäinenAuthors Info & Claims

SensorKDD '09: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data

Pages 13 - 22

https://doi.org/10.1145/1601966.1601972

Published: 28 June 2009 Publication History

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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.

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Cited By

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Sun ZTang JQiao JCui C(2020)Review of Concept Drift Detection Method for Industrial Process Modeling2020 39th Chinese Control Conference (CCC)10.23919/CCC50068.2020.9189106(5754-5759)Online publication date: Jul-2020
https://doi.org/10.23919/CCC50068.2020.9189106
Mittal VSrivastava R(2020)A Taxonomy of Methods for Handling Data Streams in Presence of Concepts DriftsFuturistic Trends in Networks and Computing Technologies10.1007/978-981-15-4451-4_41(521-531)Online publication date: 22-Apr-2020
https://doi.org/10.1007/978-981-15-4451-4_41
Song YZhang GLu HLu J(2019)A Noise-tolerant Fuzzy c-Means based Drift Adaptation Method for Data Stream Regression2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2019.8859005(1-6)Online publication date: Jun-2019
https://doi.org/10.1109/FUZZ-IEEE.2019.8859005
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Index Terms

Handling outliers and concept drift in online mass flow prediction in CFB boilers
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

SensorKDD '09: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data

June 2009

150 pages

ISBN:9781605586687

DOI:10.1145/1601966

Conference Chairs:
Olufemi A. Omitaomu
Oak Ridge National Laboratory, TN
,
Auroop R. Ganguly
Oak Ridge National Laboratory, TN
,
Joao Gama
University of Porto, Portugal
,
Ranga Raju Vatsavai
Oak Ridge National Laboratory, TN
,
Nitesh V. Chawla
University of Notre Dame, IN
,
Mohamed Medhat Gaber
Monash University, Australia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2009

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KDD09: The 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

June 28, 2009

Paris, France

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Cited By

View all

Sun ZTang JQiao JCui C(2020)Review of Concept Drift Detection Method for Industrial Process Modeling2020 39th Chinese Control Conference (CCC)10.23919/CCC50068.2020.9189106(5754-5759)Online publication date: Jul-2020
https://doi.org/10.23919/CCC50068.2020.9189106
Mittal VSrivastava R(2020)A Taxonomy of Methods for Handling Data Streams in Presence of Concepts DriftsFuturistic Trends in Networks and Computing Technologies10.1007/978-981-15-4451-4_41(521-531)Online publication date: 22-Apr-2020
https://doi.org/10.1007/978-981-15-4451-4_41
Song YZhang GLu HLu J(2019)A Noise-tolerant Fuzzy c-Means based Drift Adaptation Method for Data Stream Regression2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2019.8859005(1-6)Online publication date: Jun-2019
https://doi.org/10.1109/FUZZ-IEEE.2019.8859005
Appice AGuccione PMalerba DCiampi A(2014)Dealing with temporal and spatial correlations to classify outliers in geophysical data streamsInformation Sciences: an International Journal10.1016/j.ins.2013.12.009285:C(162-180)Online publication date: 20-Nov-2014
https://dl.acm.org/doi/10.1016/j.ins.2013.12.009
Maslov APechenizkiy MKärkkäinen TTähtinen MDas DGanguly AChandola VOmitaomu OSteinhaeuser KGama JVatsavai RGaber MChawla N(2012)Quantile index for gradual and abrupt change detection from CFB boiler sensor data in online settingsProceedings of the Sixth International Workshop on Knowledge Discovery from Sensor Data10.1145/2350182.2350185(25-33)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2350182.2350185
Lee JMagoules F(2012)Detection of Concept Drift for Learning from Stream DataProceedings of the 2012 IEEE 14th International Conference on High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems10.1109/HPCC.2012.40(241-245)Online publication date: 25-Jun-2012
https://dl.acm.org/doi/10.1109/HPCC.2012.40
Ivannikov AJegoroff MKärkkäinen T(2012)Real-time mass flow estimation in circulating fluidized bedProceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects10.1007/978-3-642-31488-9_9(103-112)Online publication date: 13-Jul-2012
https://dl.acm.org/doi/10.1007/978-3-642-31488-9_9
Pechenizkiy MBakker JŽliobaitė IIvannikov AKärkkäinen T(2010)Online mass flow prediction in CFB boilers with explicit detection of sudden concept driftACM SIGKDD Explorations Newsletter10.1145/1809400.180942311:2(109-116)Online publication date: 27-May-2010
https://dl.acm.org/doi/10.1145/1809400.1809423
Žliobaitė IBakker JPechenizkiy M(2009)OMFPProceedings of the 12th International Conference on Discovery Science10.1007/978-3-642-04747-3_22(272-286)Online publication date: 7-Oct-2009
https://dl.acm.org/doi/10.1007/978-3-642-04747-3_22

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Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift

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