Fault Analysis of the Beam Acceleration Control System at the European XFEL using Data Mining

Grunhagen, Arne; Branlard, Julien; Eichler, Annika; Martino, Gianluca; Fey, Görschwin; Tropmann-Frick, Marina

doi:10.1109/ats52891.2021.00023

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…We use the tsfresh Python package [18] to calculate a set of statistics from the data frames. The selected statistics correspond to the most frequently used statistics in the field of feature extraction for fault analysis [19]. Table 1 gives an overview of the extracted statistics, a short description, and, if applicable, the corresponding parameter choices.…”

Section: Statistical Feature Extractionmentioning

confidence: 99%

Data-Based Condition Monitoring and Disturbance Classification in Actively Controlled Laser Oscillators

Grünhagen,

Eichler,

Tropmann-Frick

et al. 2024

Frontiers in Artificial Intelligence and Applications

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The successful operation of the laser-based synchronization system of the European X-Ray Free Electron Laser relies on the precise functionality of numerous dynamic systems operating within closed loops with controllers. In this paper, we present how data-based machine learning methods can detect and classify disturbances to such dynamic systems based on the controller output signal. We present 4 feature extraction methods based on statistics in the time domain, statistics in the frequency domain, characteristics of spectral peaks, and the autoencoder latent space representation of the frequency domain. These feature extraction methods require no system knowledge and can easily be transferred to other dynamic systems. We combine feature extraction, fault detection, and fault classification into a comprehensive and fully automated condition monitoring pipeline. For that, we systematically compare the performance of 19 state-of-the-art fault detection and 4 classification algorithms to decide which combination of feature extraction and fault detection or classification algorithm is most appropriate to model the condition of an actively controlled phase-locked laser oscillator. Our experimental evaluation shows the effectiveness of clustering algorithms, showcasing their strong suitability in detecting perturbed system conditions. Furthermore, in our evaluation, the support vector machine proves to be the most suitable for classifying the various disturbances.

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Section: Statistical Feature Extractionmentioning

confidence: 99%

Data-Based Condition Monitoring and Disturbance Classification in Actively Controlled Laser Oscillators

Grünhagen,

Eichler,

Tropmann-Frick

et al. 2024

Frontiers in Artificial Intelligence and Applications

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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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“…Although it can be measured, the beam information was not available in the given data sets. Thus, the beam loading is considered as a repetitive disturbance, whose effect is cancelled by the mean value correction in (12).…”

Section: Residual Evaluationmentioning

confidence: 99%

“…3. As the beam signal is not changing during the short snapshot data sets that we consider, we can correct for this using (12). Furthermore, it is obvious that with the residual calculation we are in the resolution range of the considered signals as one clearly sees quantization effects.…”

Section: Statisticsmentioning

confidence: 99%

“…Mainly data-driven methods have been used, exploiting tools from machine learning. Application examples range from magnet fault detection at CERN [9] and at the Advanced Photon Source at Argonne National Laboratory [10] over the detection of faulty beam position monitors at the Large Hadron Collider [11] to fault detection at the digital electronics level [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Anomaly Detection at the European XFEL using a Parity Space based Method

Eichler¹,

Branlard²,

Timm³

2022

Preprint

Self Cite

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A novel approach to detect anomalies in superconducting radio-frequency cavities is presented, based on the parity space method with the goal to detect quenches and distinguish them from other anomalies. The model-based parity space method relies on analytical redundancy and generates a residual signal computed from measurable RF waveforms. The residual is a sensitive indicator of deviation from the model and provides different signatures for different types of anomalies. This new method not only helps with detecting faults, but also provides a catalogue of unique signatures, based on the detected fault. The method was experimentally verified at the European X-ray Free Electron Laser (EuXFEL). Various types of anomalies incorrectly detected as quenches by the current quench detection system are analysed using this new approach.

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Fault Analysis of the Beam Acceleration Control System at the European XFEL using Data Mining

Cited by 5 publications

References 6 publications

Data-Based Condition Monitoring and Disturbance Classification in Actively Controlled Laser Oscillators

Data-Based Condition Monitoring and Disturbance Classification in Actively Controlled Laser Oscillators

A smart alarm for particle accelerator beamline operations

Anomaly Detection at the European XFEL using a Parity Space based Method

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