Real electronic signal data from particle accelerator power systems for machine learning anomaly detection

Radaideh, Majdi I.; Pappas, Chris H.; Cousineau, S.

doi:10.1016/j.dib.2022.108473

Cited by 12 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Discussionmentioning

confidence: 98%

“…Therefore, it is worth highlighting an important difference between the data we used here (from the RFTF) and the data we published recently from the main 15 HVCMs powering the SNS (Radaideh, Pappas, & Cousineau, 2022). The previously published data (Radaideh, Pappas, & Cousineau, 2022) have many fault events recorded from multiple modules, however, the data are not continuous in time as only the pulse before the fault event is available, i.e., that data cannot be used for prognosis applications but can be used for multi-class fault classification. The current RFTF data are steamed with a much better time continuity, where system configuration and settings remain almost the same, which make them a good fit for prognosis even though the number of fault sources/varieties is very limited, i.e., cannot be used for multi-class classification.…”

Section: Discussionmentioning

confidence: 98%

See 1 more Smart Citation

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

et al. 2022

View full text Add to dashboard Cite

High voltage converter modulators (HVCM) provide power to the accelerating cavities of the Spallation Neutron Source (SNS) facility. HVCMs experience catastrophic failures, which increase the downtime of the SNS and reduce beam time. The faults may occur due to different reasons including failures of the resonant capacitor, core saturation due to the magnetic flux, insulated-gate bipolar transistor (IGBT) failures, and others. We recently have setup a HVCM test stand to develop and test machine learning models for anomaly detection and fault prognostics. In this work, we propose binary classifiers and autoencoder architectures based on convolutional (CNN) and feedforward neural networks (FNN) to facilitate distinguishing normal from faulty waveforms coming from the HVCM during operation. The results indicate that the CNN binary classifier is the best model among the four showing very stable performance in the training and testing sets with impressive precision and recall metrics, reaching up to 99% with a very small uncertainty. The FNN classifier shows the least performance with a large uncertainty in its metrics. The performances of the two autoencoders based on CNN and FNN were in between, showing very good performance nonetheless.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 98%

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

et al. 2022

View full text Add to dashboard Cite

show abstract

“…There are a total of 15 HVCMs in the SNS, driving a total of 92 klystrons, where the HVCM powering the RFQ section (3 klystrons) was the subject of the analysis in our previous effort (Radaideh, Pappas, Walden, et al, 2022). We recently shared the normal and fault data collected from the 15 HVCMs of the SNS (Radaideh, Pappas, & Cousineau, 2022), collected over 2 years with the data being sparse in time (recorded signals can be separated by hours and even days). Given their time sparsity, that data (Radaideh, Pappas, & Cousineau, 2022) or models can be used for fault classification and identification but not for early fault detection (or prognosis), which is the motivation behind this work.…”

Section: Methodsmentioning

confidence: 99%

Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning

Radaideh¹,

Pappas²,

Wezensky³

et al. 2023

IJPHM

View full text Add to dashboard Cite

Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 early fault detection experiments, where fault precursors are introduced in the system to a degree enough to cause degradation in the waveform signals, but not enough to reach a real fault. Nine different machine learning techniques based on ensemble trees, convolutional neural networks, support vector machines, and hierarchical voting ensembles are proposed to detect the fault precursors. Although all 9 models have shown a perfect and identical performance during the training and testing phase, the performance of most models has decreased in the next test phase once they got exposed to realworld data from the 21 experiments. The hierarchical voting ensemble, which features multiple layers of diverse models, maintains a distinguished performance in early detection of the fault precursors with 95% success rate (20/21 tests), followed by adaboost and extremely randomized trees with 52% and 48% success rates, respectively. The support vector machine models were the worst with only 24% success rate (5/21 tests). The study concluded that a successful implementation of machine learning in the SNS or particle accelerator power systems would require a major upgrade in the controller and the data acquisition system to facilitate streaming and handling big data for the machine learning models. In addition, this study shows that the best performing models were diverse and based on the ensemble concept to reduce the bias and hyperparameter sensitivity of individual models.

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

Real electronic signal data from particle accelerator power systems for machine learning anomaly detection

Cited by 12 publications

References 4 publications

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning

A smart alarm for particle accelerator beamline operations

Contact Info

Product

Resources

About