Model Learning Algorithms for Anomaly Detection in CERN Control Systems

Tilaro, Filippo; Bradu, Benjamin; Gonzalez-Berges, Manuel; Roshchin, Mikhail; Varela, Fernando

doi:10.18429/jacow-icalepcs2017-tucpa04

Cited by 2 publications

(1 citation statement)

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“…Given that radio-frequency (RF) cavities are the fundamental building blocks of particle accelerators, and given that these devices generate information-rich data, a lot of research has been directed toward detection, isolation, classification, and prediction of anomalies in RF systems [3][4][5][6]. Recent work also applies anomaly detection methods to superconducting magnets [7], to identify and remove malfunctioning beam position monitors (BPMs) [8], and classify or predict errant signals [9,10], among many other applications [11][12][13][14][15].…”

Section: Related Workmentioning

confidence: 99%

A smart alarm for particle accelerator beamline operations

Tennant

Freeman²,

Kazimi³

et al. 2023

Mach. Learn.: Sci. Technol.

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We present the initial results of a proof-of-concept ‘smart alarm’ for the Continuous Electron Beam Accelerator Facility injector beamline at Jefferson Lab. To minimize machine downtime and improve operational efficiency, an autonomous alarm system able to identify and diagnose unusual machine states is needed. Our approach leverages a trained neural network capable of alerting operators (a) when an anomalous condition exists in the beamline and (b) identifying the element setting that is the root cause. The tool is based on an inverse model that maps beamline readings (diagnostic readbacks) to settings (beamline attributes operators can modify). The model takes as input readings from the machine and computes machine settings which are compared to control setpoints. Instances where predictions differ from setpoints by a user-defined threshold are flagged as anomalous. Given data corresponding to 354 anomalous injector configurations, the model can narrow the root cause of an anomalous condition to three potential candidates with 94.6% accuracy. Furthermore, compared to the current method of identifying anomalous conditions which raises an alarm when machine parameters drift outside their normal tolerances, the data-driven model can identify 83% more anomalous conditions.

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Section: Related Workmentioning

confidence: 99%

A smart alarm for particle accelerator beamline operations

Tennant

Freeman²,

Kazimi³

et al. 2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

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Beam-based rf station fault identification at the SLAC Linac Coherent Light Source

Humble

O'Shea²,

Colocho³

et al. 2022

Phys. Rev. Accel. Beams

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Significant advances in utilizing deep learning for anomaly detection have been made in recent years. However, these methods largely assume the existence of a normal training set (i.e., uncontaminated by anomalies) or even a completely labeled training set. In many complex engineering systems, such as particle accelerators, labels are sparse and expensive; in order to perform anomaly detection in these cases, we must drop these assumptions and utilize a completely unsupervised method. This paper introduces the Resilient Variational Autoencoder (ResVAE), a deep generative model specifically designed for anomaly detection. ResVAE exhibits resilience to anomalies present in the training data and provides feature-level anomaly attribution. During the training process, ResVAE learns the anomaly probability for each sample as well as each individual feature, utilizing these probabilities to effectively disregard anomalous examples in the training data. We apply our proposed method to detect anomalies in the accelerator status at the SLAC Linac Coherent Light Source (LCLS). By utilizing shot-to-shot data from the beam position monitoring system, we demonstrate the exceptional capability of ResVAE in identifying various types of anomalies that are visible in the accelerator.

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Model Learning Algorithms for Anomaly Detection in CERN Control Systems

Cited by 2 publications

References 0 publications

A smart alarm for particle accelerator beamline operations

A smart alarm for particle accelerator beamline operations

Beam-based rf station fault identification at the SLAC Linac Coherent Light Source

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