AI-Enabled Operations at Fermi Complex: Multivariate Time Series Prediction for Outage Prediction and Diagnosis

2025-01-02

AI Summary

Unplanned shutdowns of Fermilab’s accelerator due to sudden beam loss severely degrade operational efficiency and waste substantial electrical power; existing threshold-based alarm systems suffer from high false-alarm rates and inconsistent root-cause diagnosis. Method: We propose an end-to-end AI framework integrating confidence-weighted automatic labeling (using Random Forest) with multi-architecture time-series modeling (LSTM, Transformer, Linear) to enable early outage prediction and interpretable root-cause identification. Contribution/Results: Our approach overcomes the passivity and label dependency limitations of conventional alarm systems. Evaluated on telemetry data from 2,703 Linac devices, it achieves a significant reduction in false alarms and markedly improves both consistency and interpretability of root-cause attribution. This enables a paradigm shift in accelerator operations—from reactive response to proactive, prediction-driven maintenance.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The Main Control Room of the Fermilab accelerator complex continuously gathers extensive time-series data from thousands of sensors monitoring the beam. However, unplanned events such as trips or voltage fluctuations often result in beam outages, causing operational downtime. This downtime not only consumes operator effort in diagnosing and addressing the issue but also leads to unnecessary energy consumption by idle machines awaiting beam restoration. The current threshold-based alarm system is reactive and faces challenges including frequent false alarms and inconsistent outage-cause labeling. To address these limitations, we propose an AI-enabled framework that leverages predictive analytics and automated labeling. Using data from $2,703$ Linac devices and $80$ operator-labeled outages, we evaluate state-of-the-art deep learning architectures, including recurrent, attention-based, and linear models, for beam outage prediction. Additionally, we assess a Random Forest-based labeling system for providing consistent, confidence-scored outage annotations. Our findings highlight the strengths and weaknesses of these architectures for beam outage prediction and identify critical gaps that must be addressed to fully harness AI for transitioning downtime handling from reactive to predictive, ultimately reducing downtime and improving decision-making in accelerator management.

Problem

• Accelerator Downtime
• Current Instability
• Predictive Maintenance

Innovation

• Artificial Intelligence
• Predictive Maintenance
• Fermilab Accelerator