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

arXiv: https://arxiv.org/abs/2304.10639 · HTML: https://mlsanity.com/paper/2304.10639/

## 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 (most cited first, 10 of 10)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Visualizing the Loss Landscape of Neural Nets](https://mlsanity.com/paper/1712.09913/) (2017)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Learning Deep Representations of Appearance and Motion for Anomalous Event Detection](https://mlsanity.com/paper/1510.01553/) (2015)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification](https://mlsanity.com/paper/2109.10254/) (2021)
- [Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator](https://mlsanity.com/paper/2110.12006/) (2021)
- [Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions](https://mlsanity.com/paper/2202.09353/) (2022)

## Built on by (most cited first, 3 of 3)

- [Variational Digital Twins](https://mlsanity.com/paper/2507.01047/) (2025)
- [Physics-based Digital Twins for Integrated Thermal Energy Systems Using Active Learning](https://mlsanity.com/paper/2605.06756/) (2026)
- [Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source](https://mlsanity.com/paper/2605.31259/) (2026)

## 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