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Extending Process Monitoring to Simultaneous False Alarm Rejection and Fault Identification (FARFI)

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2016)

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Abstract

A new framework for extending Statistical Process Monitoring (SPM) to simultaneous False Alarm Rejection and Fault Identification (FARFI) is presented in this paper. This is motivated by the possibly large negative impact on product quality, process safety, and profitability resulting from incorrect control actions induced by false alarms—especially for batch processes. The presented FARFI approach adapts the classification model already used for fault identification to simultaneously perform false alarm rejection by adding normal operation as an extra data class. As no additional models are introduced, the complexity of the overall SPM system is not increased.

Two case studies demonstrate the large potential of the FARFI approach. The best models reject more than \(94\,\%\) of the false alarms while their fault identification accuracy (\(>95\,\%\)) is not impacted. However, results also indicate that not all classifier types perform equally well. Care should be taken to employ models that can deal with the added classification challenges originating from the introduction of the false alarm class.

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Acknowledgements

Work supported in part by Project PFV/10/002 (OPTEC Optimization in Engineering Center) of the Research Council of the KU Leuven and Project IAP VII/19 (DYSCO Dynamical Systems, Control and Optimization) of the Belgian Program on Interuniversity Poles of Attraction initiated by the Belgian Federal Science Policy Office. The authors assume scientific responsibility.

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Authors and Affiliations

  1. Department of Chemical Engineering, Chemical and Biochemical Process Technology and Control (BioTeC+), KU Leuven Technology Campus Ghent, Gebroeders De Smetstraat 1, 9000, Ghent, Belgium

    Geert Gins, Sam Wuyts, Sander Van den Zegel & Jan Van Impe

Authors
  1. Geert Gins
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  2. Sam Wuyts
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  3. Sander Van den Zegel
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  4. Jan Van Impe
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Correspondence to Jan Van Impe .

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Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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Gins, G., Wuyts, S., Van den Zegel, S., Van Impe, J. (2016). Extending Process Monitoring to Simultaneous False Alarm Rejection and Fault Identification (FARFI). In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_25

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  • DOI: https://doi.org/10.1007/978-3-319-41561-1_25

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