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.
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