TY - JOUR
T1 - Dynamic alarm monitoring with data-driven ellipsoidal threshold learning
AU - Cui, Kaixin
AU - Wu, Wenjing
AU - Shang, Jun
AU - Shi, Dawei
N1 - Publisher Copyright:
© 2025 Elsevier Ltd
PY - 2025/5
Y1 - 2025/5
N2 - 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.
AB - 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.
KW - Alarm systems
KW - Data-driven ellipsoidal threshold learning
KW - Dynamic alarm limits
UR - http://www.scopus.com/inward/record.url?scp=85217896023&partnerID=8YFLogxK
U2 - 10.1016/j.conengprac.2025.106282
DO - 10.1016/j.conengprac.2025.106282
M3 - Article
AN - SCOPUS:85217896023
SN - 0967-0661
VL - 158
JO - Control Engineering Practice
JF - Control Engineering Practice
M1 - 106282
ER -