A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework
This work addresses quench prediction for hardware commissioning at CERN, but it is incremental as it builds on existing frameworks and methods.
The authors tackled the problem of predicting quenches in the TE-MPE-EE at CERN by developing a web application based on an ELQA framework and an LSTM neural network, presenting a conceptual implementation without concrete numerical results.
This article presents a development of web application for quench prediction in \gls{te-mpe-ee} at CERN. The authors describe an ELectrical Quality Assurance (ELQA) framework, a platform which was designed for rapid development of web integrated data analysis applications for different analysis needed during the hardware commissioning of the Large Hadron Collider (LHC). In second part the article describes a research carried out with the data collected from Quench Detection System by means of using an LSTM recurrent neural network. The article discusses and presents a conceptual work of implementing quench prediction application for \gls{te-mpe-ee} based on the ELQA and quench prediction algorithm.