Speaker
Description
Detecting quenches in superconducting (SC) magnets during training is a challenging process that involves capturing physical events that occur at different frequencies and appear as various signal features. These events may be correlated across instrumentation type,thermal cycle, and ramp. These events together build a more complete picture of continuous
processes occurring in the magnet, and may allow us to flag potential precursors for quench detection. We build upon our existing work on unsupervised autoencoders for acoustic sensors and quench antenna (QA) by first establishing a supervised ML training pipeline. We show the results of an event tagging, analysis, and simulation framework which are used concurrently to build a training dataset for a supervised implementation. We then show how this supervised training can be used as a prior in a semi-supervised framework and compare this to the unsupervised auto-encoder performance. This allows us to have a more concrete understanding of the performance of our algorithms relative to physical events occurring in the magnet, and also provides a baseline software tool to generically evaluate autoencoders under completely unsupervised, supervised, and semi-supervised training conditions.
