Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
This work addresses fault detection in high-energy physics equipment, offering a novel deep learning-based approach that could enhance safety and efficiency, though it appears incremental as it adapts existing methods to a new domain.
The paper tackled monitoring and fault protection for the Large Hadron Collider superconducting magnets by applying LSTM recurrent neural networks to model voltage time series, achieving a best RMSE of 0.00104 with a specific network configuration.
The superconducting LHC magnets are coupled with an electronic monitoring system which records and analyses voltage time series reflecting their performance. A currently used system is based on a range of preprogrammed triggers which launches protection procedures when a misbehavior of the magnets is detected. All the procedures used in the protection equipment were designed and implemented according to known working scenarios of the system and are updated and monitored by human operators. This paper proposes a novel approach to monitoring and fault protection of the Large Hadron Collider (LHC) superconducting magnets which employs state-of-the-art Deep Learning algorithms. Consequently, the authors of the paper decided to examine the performance of LSTM recurrent neural networks for modeling of voltage time series of the magnets. In order to address this challenging task different network architectures and hyper-parameters were used to achieve the best possible performance of the solution. The regression results were measured in terms of RMSE for different number of future steps and history length taken into account for the prediction. The best result of RMSE=0.00104 was obtained for a network of 128 LSTM cells within the internal layer and 16 steps history buffer.