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

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.Comment: Related to arXiv:1702.0083

Magnetic properties of a nanocrystalline material for current derivative sensors of magnets protection systems

Nanocrystalline materials are becoming ever more broadly used in transformer-based transducers due to their low losses, high relative permeability and high saturation flux density. In this paper, the magnetic characterization of one of these materials is presented by highlighting its influence on the performance of a current derivative sensor. This sensor was recently prototyped at CERN in the framework of the consolidation activity on the quench protection of superconducting magnets for the high-luminosity upgrade of the Large Hadron Collider. The performance is analyzed in terms of linearity and dynamic response

Towards a muon collider

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10 TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work

Magnetic field mapper based on rotating coils

This thesis presents a magnetic field mapper based on rotating coils. The requirements, the architecture, the conceptual design, and the prototype for straight magnets were shown. The proposed system is made up of a rotating coil transducer and a train-like system for longitudinal motion and positioning inside magnet bore. The mapper allows a localized measurement of magnetic fields and the variation of the harmonic multipole content in the magnet ends. The proof-of-principle demonstration and the experimental characterization of the rotating-coil transducer specifically conceived for mapping validated the main objective of satisfying the magnetic measurement needs of the next generation of compact accelerators

Naturalismo e storicismo nell'etnologia

La nuova edizione del testo pubblicato per la prima e unica volta nel 1941

Software for measurement automation: A review of the state of the art

The world of software for measurement and test applications is analyzed by considering two main distinct groups of solutions related to the scenario on the market and to the state of the art in research. For the first group, after defining suitable decision criteria for analyzing the related products, the most innovative and promising solutions of manufacturers leaders are reviewed. In the second group, software tools and applications for instrumentation and measurement research are examined, by looking for main trends in the related scenario and a synthesis of the most promising advancements

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

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

Flexible test automation: a software framework for easily developing measurement applications

In laboratory management of an industrial test division, a test laboratory, or a research center, one of the main activities is producing suitable software for automatic benches by satisfying a given set of requirements. This activity is particularly costly and burdensome when test requirements are variable over time. If the batches of objects have small size and frequent occurrence, the activity of measurement automation becomes predominating with respect to the test execution. Flexible Test Automation shows the development of a software framework as a useful solution to satisfy this exigency. The framework supports the user in producing measurement applications for a wide range of requirements with low effort and development time

Unscented transform-based uncertainty analysis of rotating coil transducers for field mapping

The uncertainty of a rotating coil transducer for magnetic field mapping is analyzed. Unscented transform and statistical design of experiments are combined to determine magnetic field expectation, standard uncertainty, and separate contributions of the uncertainty sources. For nonlinear measurement models, the unscented transform-based approach is more error-proof than the linearization underlying the “Guide to the expression of Uncertainty in Measurements” (GUMs), owing to the absence of model approximations and derivatives computation. When GUM assumptions are not met, the deterministic sampling strategy strongly reduces computational burden with respect to Monte Carlo-based methods proposed by the Supplement 1 of the GUM. Furthermore, the design of experiments and the associated statistical analysis allow the uncertainty sources domain to be explored efficiently, as well as their significance and single contributions to be assessed for an effective setup configuration. A straightforward experimental case study highlights that a one-order-of-magnitude reduction in the relative uncertainty of the coil area produces a decrease in uncertainty of the field mapping transducer by a factor of 25 with respect to the worst condition. Moreover, about 700 trials and the related processing achieve results corresponding to 5 × 106 brute-force Monte Carlo simulations

A rotating coil transducer for magnetic field mapping

A rotating coil transducer for local measurements of magnetic field quality in magnets is proposed. The transducer is based on (i) reduced-dimension rotating coils, as required e.g. for space charge computations, (ii) accurate transport, for longitudinal displacements inside the magnet aperture, and (iii) components with magnetic compatibility for negligible interference of the measurand field. This allows magnetic measurement requirements arisen from recently developed compact accelerator systems (with curvature radii of less than 5 m) for biomedical applications and physics research to be satisfied. In the paper, after presenting requirements and conceptual design, the architecture of the transducer is illustrated. Then, the experimental validation by tests of magnetic compatibility and rotation uniformity is reported. Finally, experimental results of repeatability, accuracy, and resolution in comparison with a reference system are discussed