Uncertainty aware anomaly detection to predict errant beam pulses in the Oak Ridge Spallation Neutron Source accelerator

Blokland, W.; Rajput, Kishansingh; Schram, M.; Jeske, Torri; Ramuhalli, Pradeep; Peters, Charles C.; Yucesan, Yigit A.; Zhukov, Alexander

doi:10.1103/physrevaccelbeams.25.122802

Cited by 12 publications

(9 citation statements)

References 20 publications

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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.

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“…Neural networks (NN) have been used as surrogate models for magnet control [30] and for simulation-based optimization studies [31]. Neural networks are also being used for uncertainty aware anomaly detection to predict errant beam pulses [32], as virtual diagnostics for 4D tomographic phase space reconstructions [33], for predicting the transverse emittance of space charge dominated beams In Sections IV-B and IV-C we tune several components in this section of the accelerator. [34], and for high resolution longitudinal phase space virtual diagnostics [35].…”

Section: B Accelerator Tuning and Optimizationmentioning

confidence: 99%

“…Fig. 6 shows that Classical ES with a modified cost function, in (32), does not always remain safe and the weight w cannot be known ahead of time to guarantee best performance and safety. It turns out that choosing w ≈ 0.07 in the Classical ES scheme would deliver performance and safety comparable to that of the Safe ES controllers, but this choice would require excessive effort, or extra system knowledge, on the part of the control designer.…”

Section: B Simulated Lebt Tuningmentioning

confidence: 99%

Practically Safe Extremum Seeking

Williams

Krstić

Scheinker

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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We demonstrate the recent designs of Safe Extremum Seeking (Safe ES) on the 1 kilometer-long charged particle accelerator at the Los Alamos Neutron Science Center (LANSCE). Safe ES is a modification of ES which, in addition to minimizing an analytically unknown cost, also employs a safety filter based on an analytically unknown control barrier function (CBF) safety metric.Accelerator tuning is necessitated by the accelerators being large, with many drifting parameters due to thermal effects and degradation. At the same time, safe operation (the maintenance of state constraints) is crucial, as damage brings astronomical costs, both financially and in operation downtime.Our measured (but analytically unknown) safety metric is the beam current. We perform multivariable Safe ES on three accelerator applications, in which we adapt 4, 6, and 3 magnet strength parameters, respectively. Two of the three applications are for validated simulation models of beamlines at LANSCE: the first for the Proton Radiography (pRad) beamline of 800 MeV protons for spot size tuning; the second on a high performance code, HPSim, for tuning the low energy beam transport (LEBT) region of of 750 keV protons. The third is an experimental tuning of the steering magnets in the LEBT at LANSCE.

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“…AE and VAE models learn to reproduce the normal data samples and predict anomalies based on the reconstruction error (the error between the input data and the corresponding reproduced data). However, our previous study [4] on this application concluded that siamese neural network (SNN) models, a supervised learning method, can outperform AE models. SNN can also leverage many normal samples and avoid label imbalance as described in section 4.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce downtime, radio-activation of beam line elements, and component damage, we explored ML methods that use data from existing diagnostic sensors to predict equipment failures and errant beams. Previous studies at the SNS [3,4] show that precursor information is present in the available data, which can predict an impending fault.…”

Section: Introductionmentioning

confidence: 99%

Robust errant beam prognostics with conditional modeling for particle accelerators

Rajput,

Schram,

Blokland

et al. 2024

Mach. Learn.: Sci. Technol.

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Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised Machine Learning (ML) techniques such as Siamese Neural Network (SNN) models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ Conditional Siamese Neural Network (CSNN) models and Conditional Variational Auto Encoder (CVAE) models to predict errant beam pulses at the Spallation Neutron Source (SNS) under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

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Uncertainty aware anomaly detection to predict errant beam pulses in the Oak Ridge Spallation Neutron Source accelerator

Cited by 12 publications

References 20 publications

A smart alarm for particle accelerator beamline operations

A smart alarm for particle accelerator beamline operations

Practically Safe Extremum Seeking

Robust errant beam prognostics with conditional modeling for particle accelerators

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