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Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis

  • Marta Fullen
  • Peter Schüller
  • Oliver Niggemann
Conference paper
Part of the Technologien für die intelligente Automation book series (TIA, volume 11)

Abstract

Alarm floods are a major issue in complex industrial plants. Abundance of alarms annunciated in a short period of time can exceed the operators cognitive capabilities and lead to an increased downtime or a serious plant failure. We propose a data-driven approach to detecting and analysing the alarm floods with the goal of supporting the operator during an alarm flood. The approach is based on machine learning concepts of semi-supervised learning and case-based reasoning, and requires a small amount of expert annotations on a historical alarm flood case base. It is comprised of an offline learning stage and an online detection and root cause classification stage. The proposed approach is applied and validated on a real industrial alarm dataset.

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Notes

Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 678867.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  • Marta Fullen
    • 1
  • Peter Schüller
    • 2
  • Oliver Niggemann
    • 1
    • 3
  1. 1.Fraunhofer Application Center Industrial AutomationLemgoGermany
  2. 2.Marmara UniversityIstanbulTurkey
  3. 3.Institute Industrial ITLemgoGermany

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