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May 8 – 12, 2023
Norfolk Waterside Marriott
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Multi-Module based VAE to predict HVCM faults in the SNS accelerator

May 8, 2023, 11:45 AM
Hampton Roads VII (Norfolk Waterside Marriott)

Hampton Roads VII

Norfolk Waterside Marriott

235 East Main Street Norfolk, VA 23510
Oral Track 9 - Artificial Intelligence and Machine Learning Track 9 - Artificial Intelligence and Machine Learning


Mr Alanazi, Yasir (Jefferson Lab)


We present a Multi-Module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the High Voltage Converter Modulators (HVCMs) which have historically been a cause of major down time for the Spallation Neutron Source (SNS) facility. Previous studies using machine learning techniques were to predict faults ahead of time in the SNS accelerator using a Single Modulator. Using the proposed methodology, we can detect faults in the power signals coming from multiple HVCMs that vary in design specifications and operating conditions. By conditioning the model according to the given modulator system, we can capture different representations of the normal waveforms for multiple systems. Our experiments with the SNS experimental data show that the trained model generalizes well to detecting several fault types for different systems, which can be valuable to improve the HVCM reliability and SNS as a result. We also explore several neural network architectures in our CVAE model by visualizing their loss landscapes to study the stability and generalization of the developed models and assist in hyper-parameter optimization and model selection to produce well-performed predictions.

Consider for long presentation No

Primary author

Mr Alanazi, Yasir (Jefferson Lab)


Lu, Dan (Oak Ridge National Lab) Pappas, Chris (Oak Ridge National Lab) Radaideh, Majdi I (Oak Ridge National Lab) Rajput, Kishansingh (Thomas Jefferson National Accelerator Facility) Schram, Malachi (Thomas Jefferson National Accelerator Facility) Goldenberg, Steven (Jefferson Lab)

Presentation materials