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Sensors 2018, 18(11), 3933; https://doi.org/10.3390/s18113933

Protection of Superconducting Industrial Machinery Using RNN-Based Anomaly Detection for Implementation in Smart Sensor

1
Faculty of Computer Science, Electronics and Telecommunications, AGH University of Science and Technology, al. Adama Mickiewicza 30, 30-059 Cracow, Poland
2
Academic Computer Centre CYFRONET AGH, ul. Nawojki 11, 30-072 Cracow, Poland
3
Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, al. Adama Mickiewicza 30, 30-059 Cracow, Poland
4
CERN European Organization for Nuclear Research, CH-1211 Geneva 23, Switzerland
This paper is an extended version of “Looking for a Correct Solution of Anomaly Detection in the LHC Machine Protection System” published in the Proceedings of the 2018 International Conference on Signals and Electronic Systems (ICSES), Kraków, Poland, 10–12 September 2018.
*
Author to whom correspondence should be addressed.
Received: 27 October 2018 / Revised: 9 November 2018 / Accepted: 11 November 2018 / Published: 14 November 2018
(This article belongs to the Special Issue Real-Time Sensor Networks and Systems for the Industrial IoT)
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Abstract

Sensing the voltage developed over a superconducting object is very important in order to make superconducting installation safe. An increase in the resistive part of this voltage (quench) can lead to significant deterioration or even to the destruction of the superconducting device. Therefore, detection of anomalies in time series of this voltage is mandatory for reliable operation of superconducting machines. The largest superconducting installation in the world is the main subsystem of the Large Hadron Collider (LHC) accelerator. Therefore a protection system was built around superconducting magnets. Currently, the solutions used in protection equipment at the LHC are based on a set of hand-crafted custom rules. They were proved to work effectively in a range of applications such as quench detection. However, these approaches lack scalability and require laborious manual adjustment of working parameters. The presented work explores the possibility of using the embedded Recurrent Neural Network as a part of a protection device. Such an approach can scale with the number of devices and signals in the system, and potentially can be automatically configured to given superconducting magnet working conditions and available data. In the course of the experiments, it was shown that the model using Gated Recurrent Units (GRU) comprising of two layers with 64 and 32 cells achieves 0.93 accuracy for anomaly/non-anomaly classification, when employing custom data compression scheme. Furthermore, the compression of proposed module was tested, and showed that the memory footprint can be reduced four times with almost no performance loss, making it suitable for hardware implementation. View Full-Text
Keywords: anomaly detection; recurrent neural networks; neural networks compression; LHC anomaly detection; recurrent neural networks; neural networks compression; LHC
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wielgosz, M.; Skoczeń, A.; De Matteis, E. Protection of Superconducting Industrial Machinery Using RNN-Based Anomaly Detection for Implementation in Smart Sensor. Sensors 2018, 18, 3933.

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