mlsanityData to 14 Oct 2026

Multi-module based CVAE to predict HVCM faults in the SNS accelerator

arXiv 2304.10639 ↗First posted 20 Apr 2023cs.LGcited by 3

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Abstract (arXiv)

We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the normal waveforms and to improve the sensitivity of the model to identify a specific type of fault when we have limited samples for a given module type. We studied several neural network (NN) architectures for our CVAE model and evaluated the model performance by looking at their loss landscape for stability and generalization. Our results for the Spallation Neutron Source (SNS) experimental data show that the trained model generalizes well to detecting multiple fault types for several HVCM module types. The results of this study can be used to improve the HVCM reliability and overall SNS uptime

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 10 of its 47 references that are in the map. First posted on arXiv 20 Apr 2023.

Builds on

10 of 47 references.

References · 10

  1. 2016Variational Inference: A Review for Statisticianscited by 1,472
  2. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
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  9. 2021Uncertainty aware anomaly detection to predict errant beam pulses in the SNS acceleratorcited by 5
  10. 2022Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansionscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Variational Digital Twinscited by 4
  2. 2026Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learningcited by 0
  3. 2026Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Sourcecited by 0