Speaker
Description
Detecting quenches in superconducting (SC) magnets by non-invasive means is a challenging real-time process that involves capturing
and sorting through 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 autoencoders for acoustic sensors and quench antenna (QA), by comparing auto encoder reconstruction loss under various algorithm training conditions to event distributions generated by an event detection framework we have developed. We also highlight our work on integrating QA and acoustic data streams to develop a unified dynamic quench detection algorithm for multi-modal data. All algorithms are evaluated in a simulated real-time environment, where instrumentation data is continuously streamed into the auto-encoder. This allows us to gain 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 relative to their capture of quench precursors for SC magnets.
