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Outline of a fault diagnosis system for a large-scale board machine

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Abstract

Global competition forces the process industries to continuously optimize plant operation. One of the latest trends in efficiency and plant availability improvement is to set up fault diagnosis and maintenance systems for online industrial use. This paper presents a methodology for developing industrial fault detection and diagnosis (FDD) systems. Since model- or data-based diagnosis of all components cannot be achieved online on a large-scale basis, the focus must be narrowed down to the most likely faulty components responsible for abnormal process behavior. One of the key elements here is fault analysis. The paper describes and briefly discusses also other development phases, process decomposition and the selection of FDD methods. The paper ends with an FDD case study of a large-scale industrial board machine including a description of the fault analysis and FDD algorithms for the resulting focus areas. Finally, the testing and validation results are presented and discussed.

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Authors and Affiliations

  1. Department of Biotechnology and Chemical Technology, Aalto University, P.O. Box 16100, 00076, Aalto, Finland

    Sirkka-Liisa Jämsä-Jounela, Vesa-Matti Tikkala, Alexey Zakharov, Octavio Pozo Garcia & Helena Laavi

  2. Stora Enso Oyj, Imatra Mills, 55800, Imatra, Finland

    Tommi Myller & Tomi Kulomaa

  3. Efora Oy, 55800, Imatra, Finland

    Veikko Hämäläinen

Authors
  1. Sirkka-Liisa Jämsä-Jounela
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  2. Vesa-Matti Tikkala
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  3. Alexey Zakharov
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  4. Octavio Pozo Garcia
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  5. Helena Laavi
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  6. Tommi Myller
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  7. Tomi Kulomaa
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  8. Veikko Hämäläinen
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Correspondence to Vesa-Matti Tikkala.

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Jämsä-Jounela, SL., Tikkala, VM., Zakharov, A. et al. Outline of a fault diagnosis system for a large-scale board machine. Int J Adv Manuf Technol 65, 1741–1755 (2013). https://doi.org/10.1007/s00170-012-4296-8

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  • DOI: https://doi.org/10.1007/s00170-012-4296-8

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