Lightweight CNN-Based Anomaly Detection for High Voltage Converter Modulators in the Spallation Neutron Source

Time series anomaly detection is central to monitoring industrial and scientific equipment, where anomalous sensor readings can precede failures. As such equipment usually carries several sensors, a detector must also decide how to model the dependencies between channels. In many physical systems, normal behaviour is defined by how groups of sensors evolve together, and an anomaly can appear as the coupling or decoupling of sensors while each signal remains normal. Anomalies in popular public benchmarks, however, are mostly visible in single channels, so these benchmarks cannot show whether modelling inter-channel dependencies improves detection. We study this on the High Voltage Converter Modulators (HVCMs) of the Spallation Neutron Source (SNS), whose fourteen sensors are physically coupled and whose faults are labelled by type. Keeping a convolutional neural network (CNN) fixed, we vary only whether its blocks combine channels before or after temporal filtering, and measure detection for each SNS subsystem and fault family. The effect of this choice depends on how visible each fault family is on a single channel. Families clearly visible on one channel are detected equally well by all variants, whereas weakly separable families, such as flux faults, gain consistently when channels are combined first. Modelling the dependencies between channels therefore helps only where individual channels do not already reveal the fault. The best detector reaches an average area under the precision--recall curve of 0.816, competitive with published HVCM detectors at roughly half the parameters of a standard CNN.