A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework

TL;DR

This paper develops a web-based quench prediction application for CERN's LHC, integrating ELQA framework and LSTM neural networks to enhance hardware commissioning analysis.

Contribution

It introduces a novel integration of ELQA platform with LSTM for quench prediction, enabling rapid development of web-based diagnostic tools.

Findings

Successful implementation of LSTM for quench prediction

Enhanced data analysis capabilities during LHC commissioning

Prototype web application demonstrated effective prediction

Abstract

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.