Dynamic alarm monitoring with data-driven ellipsoidal threshold learning

Kaixin Cui, Wenjing Wu, Jun Shang, Dawei Shi*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Alarm systems are essential for the safety maintenance and health management of industrial systems. In this work, a dynamic alarm monitoring approach with data-driven ellipsoidal threshold learning is proposed, and an unknown system is directly learned using noisy data without model identification. An ellipsoid-based normal operating zone of the system variable is iteratively predicted based on system dynamics, and is updated as an external approximation of the intersection of a predicted ellipsoid and a measurement-based ellipsoid with an event-triggering condition. Then, the dynamic alarm limits are calculated for each dimension of the output by an ellipsoid-based quadratic equation, and a projection strategy from output points to the predicted ellipsoids is designed to have two different solutions to the equation. The effectiveness of the proposed dynamic alarm monitoring approach is illustrated by experimental results on the sensor fault and actuator fault detection of an ultrasonic motor with and without an event-triggering condition, respectively.

Original languageEnglish
Article number106282
JournalControl Engineering Practice
Volume158
DOIs
Publication statusPublished - May 2025

Keywords

  • Alarm systems
  • Data-driven ellipsoidal threshold learning
  • Dynamic alarm limits

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