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

利用基于条件变分自编码器的多模块框架检测来自多个高压换流器调制器的电力信号中的异常。通过以特定的换流器类型为条件模型，可以捕获正常波形的不同表示，并在给定模块类型的样本有限时提高模型对特定故障的敏感性。研究了几种神经网络架构用于我们的 CVAE 模型，并通过观察其稳定性和泛化性能来评估模型性能。通过实验室实验数据表明，训练模型能够很好地推广到多个高压换流器模块类型的多个故障类型检测。这项研究的结果可以用于提高高压换流器调制器的可靠性和整体的 SNS 连续运行时间。