IPAC'23 - 14th International Particle Accelerator Conference

Initial results of applying an autoencoder to detect anomalies in the air conditioning systems of the Brookhaven accelerator complex

THPL013

11 May 2023, 16:30

2h

Sala Laguna

Poster Presentation MC6.A27: Machine Learning and Digital Twin Modelling Thursday Poster Session

Speaker

Vincent Schoefer (Brookhaven National Laboratory)

Description

The Collider Accelerator Complex at Brookhaven National Lab (BNL) contains millions of control points. Monitoring tolerances for these control points is crucial for the system and is a challenging task. Catching early signs of failures in those systems will be very beneficial as they can save extensive downtime. Anomaly detection in particle accelerators has been highlighted and can significantly impact the system. Autoencoder is one of the most commonly used techniques for detecting anomalies. In this contribution, we apply an autoencoder method to analyze the historical data for runs 21 and 22 to find precursors for trips (and actual trips) of Air Conditioning (AC) systems based on local thermostat readbacks. Results from the existing system are presented, showing that the new method can catch early signs of AC trips so that advance notices can be sent for the operators to take prompt action.