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

Majdi I. Radaideh; Chris Pappas; Sarah Cousineau

doi:10.1016/j.dib.2022.108473

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

Majdi I. Radaideh, Chris Pappas, Sarah Cousineau

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

This article describes real time series datasets collected from the high voltage converter modulators (HVCM) of the Spallation Neutron Source facility. HVCMs are used to power the linear accelerator klystrons, which in turn produce the high-power radio frequency to accelerate the negative hydrogen ions (H⁻). Waveform signals have been collected from the operation of more than 15 HVCM systems categorized into four major subsystems during the years 2020-2022. The data collection process occurred in the Spallation Neutron Source facility of Oak Ridge, Tennessee in the United States. For each of the four subsystems, there are two datasets. The first one contains the waveform signals, while the second contains the label of the waveform, whether it has a normal or faulty signal. A variety of waveforms are included in the datasets including insulated-gate bipolar transistor (IGBT) currents in three phases, magnetic flux in the three phases, modulator current and voltage, cap bank current and voltage, and time derivative change of the modulator voltage. The datasets provided are useful to test and develop machine learning and statistical algorithms for applications related to anomaly detection, system fault detection and classification, and signal processing.

Original language	English
Article number	108473
Journal	Data in Brief
Volume	43
DOIs	https://doi.org/10.1016/j.dib.2022.108473
State	Published - Aug 2022
Externally published	Yes

Funding

The authors are grateful for support from the Neutron Sciences Directorate at ORNL in the investigation of this work. This work was supported by the DOE Office of Science under grant DE-SC0009915 (Office of Basic Energy Sciences, Scientific User Facilities program). A portion of this research used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory. Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ).

Funders	Funder number
U.S. Department of Energy
Office of Science	DE-SC0009915
Basic Energy Sciences
Oak Ridge National Laboratory

Keywords

Anomaly detection
Digital signal processing
Fault classification
High voltage converter modulators
Machine Learning
Spallation neutron source

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10.1016/j.dib.2022.108473

Cite this

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title = "Real electronic signal data from particle accelerator power systems for machine learning anomaly detection",

abstract = "This article describes real time series datasets collected from the high voltage converter modulators (HVCM) of the Spallation Neutron Source facility. HVCMs are used to power the linear accelerator klystrons, which in turn produce the high-power radio frequency to accelerate the negative hydrogen ions (H−). Waveform signals have been collected from the operation of more than 15 HVCM systems categorized into four major subsystems during the years 2020-2022. The data collection process occurred in the Spallation Neutron Source facility of Oak Ridge, Tennessee in the United States. For each of the four subsystems, there are two datasets. The first one contains the waveform signals, while the second contains the label of the waveform, whether it has a normal or faulty signal. A variety of waveforms are included in the datasets including insulated-gate bipolar transistor (IGBT) currents in three phases, magnetic flux in the three phases, modulator current and voltage, cap bank current and voltage, and time derivative change of the modulator voltage. The datasets provided are useful to test and develop machine learning and statistical algorithms for applications related to anomaly detection, system fault detection and classification, and signal processing.",

keywords = "Anomaly detection, Digital signal processing, Fault classification, High voltage converter modulators, Machine Learning, Spallation neutron source",

author = "Radaideh, {Majdi I.} and Chris Pappas and Sarah Cousineau",

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

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Keywords

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