Speaker
Osama Mohsen
(Argonne National Laboratory)
Description
In accelerators facilities, unexpected failures of water pumps can lead to overheating, unplanned downtime, and costly repairs. In this study, we present a novel approach for real-time monitoring of water pump vibrations to detect anomalies indicative of impending mechanical failures. We employ simple vibration sensors combined with machine learning algorithms to identify patterns and deviation from normal operating conditions. Implementation of this anomaly detection framework can significantly enhance the operational efficiency and uptime of accelerator facilities by reducing unplanned outages and extending the lifespan of water pump equipment.
| Region represented | America |
|---|---|
| Paper preparation format | LaTeX |
Author
Osama Mohsen
(Argonne National Laboratory)
Co-authors
Michael Borland
(Argonne National Laboratory)
Yine Sun
(Argonne National Laboratory)
