Particle accelerators are notoriously complex and require constant monitoring to ensure smooth operation. However, researchers at the SLAC National Accelerator Laboratory have developed an artificial intelligence (AI) algorithm that can mimic human operators and keep these mile-long accelerators healthy. The algorithm continuously monitors the accelerator’s performance, alerting operators to any drops in efficiency and identifying the specific subsystem responsible. This breakthrough could simplify accelerator operation, reduce downtime, and improve the scientific data collected. The findings have been published in Physical Review Accelerators and Beams.
The AI algorithm provides valuable insights to operators, guiding them on which components to switch off and replace to ensure uninterrupted operation. By improving reliability, more subsystems can remain online, allowing the accelerator to reach its maximum operating capability. This AI approach has the potential to benefit a range of complex systems, from experimental facilities and advanced manufacturing plants to the electric grid and nuclear power plants.
Modern accelerators generate a massive amount of data, overwhelming small operations teams tasked with real-time monitoring and fault avoidance. At the Linac Coherent Light Source, for example, faults in the radiofrequency (RF) stations that accelerate electrons often lead to downtime and performance degradation. While an existing automated algorithm attempts to identify RF station problems, nearly 70% of its predictions are false positives, necessitating manual inspection.
Inspired by human operators, the AI algorithm simultaneously runs anomaly detection algorithms on RF station diagnostics and shot-to-shot measurements of the final beam quality. A fault is only predicted when both algorithms identify anomalies concurrently. This approach, now incorporated into the control room, can be fully automated and provides more accurate identifications with fewer false positives compared to relying solely on RF station diagnostics.
Furthermore, recent patent-pending work has extended this coincidence concept to incorporate deep-learning algorithms like neural networks. These advanced algorithms can identify faults in raw, unlabeled data without requiring expert input. The researchers envision broad applications for these machine learning-driven algorithms across various complex systems in science and industry.
The successful development of this AI algorithm represents a significant milestone in the quest for efficient and reliable particle accelerator operation. By leveraging the power of artificial intelligence, operators can streamline their monitoring processes, optimize performance, and ensure uninterrupted data collection for scientific research. As AI continues to advance, it holds the potential to transform not only particle accelerators but also a wide range of other complex systems critical to our modern world.
