  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
