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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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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Fullen, M., Schüller, P., Niggemann, O. (2020). Semi-supervised Case-based Reasoning Approach to Alarm Flood Analysis. In: Beyerer, J., Maier, A., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 11. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59084-3_7
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DOI: https://doi.org/10.1007/978-3-662-59084-3_7
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