The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

TL;DR

This paper presents a GRU-based anomaly detection system with adaptive quantization for CERN superconducting magnets, outperforming traditional OC-SVM models in stability and accuracy across multiple datasets.

Contribution

The authors developed a novel GRU-based anomaly detector with adaptive quantization and a parameter selection method, demonstrating superior performance over OC-SVM models.

Findings

The detector achieved F1 scores close to 1 on all test sets.

Adaptive quantization parameters significantly influence detection performance.

The proposed system outperforms OC-SVM in stability and accuracy across datasets.

Abstract

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets was intended for real-life experiments and model training. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the best_length setup of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.