STFC/RAL/ISIS, Chilton, Didcot, Oxon, United Kingdom
CERN, Meyrin, Switzerland
Universität Stuttgart, Stuttgart, Germany
Machine learning algorithms enable failure prediction of large-scale, distributed systems using historical time-series datasets. Although unsupervised learning algorithms represent a possibility to detect an evolving variety of anomalies, they do not provide links between detected data events and system failures. Additional system knowledge is required for machine learning algorithms to determine the nature of detected anomalies, which may represent either healthy system behavior or failure precursors. However, knowledge on failure behavior is expensive to obtain and might only be available upon pre-selection of anomalous system states using unsupervised algorithms. Moreover, system knowledge obtained from evaluation of system states needs to be appropriately provided to the algorithms to enable performance improvements. In this paper, we will present an approach to efficiently configure the integration of system knowledge into unsupervised anomaly detection algorithms for failure prediction. The methodology is based on simulations of failure modes of electronic circuits. Triggering system failures based on synthetically generated failure behaviors enables analysis of the detectability of failures and generation of different types of datasets containing system knowledge. In this way, the requirements for type and extend of system knowledge from different sources can be determined, and suitable algorithms allowing the integration of additional data can be identified.
SARAO, Cape Town, South Africa
The 64 receptor (with 20 more being built) radio telescope in the Karoo, South Africa, comprises a large number of devices and components connected to the Control-and-Monitoring (CAM) system via the Karoo Array Telescope Communication Protocol (KATCP). KATCP is used extensively for internal communications between CAM components and other subsystems. A KATCP interface exposes requests and sensors; sampling strategies are set on sensors, ranging from several updates per second to infrequent on-change updates. The sensor samples are of different types, from small integers to text fields. The samples and associated timestamps are permanently stored and made available for scientists, engineers and operators to query and analyze. This is a presentation on how to apply Machine Learning tools which utilize data-driven algorithms and statistical models to analyze sensor data sets and then draw inferences from identified patterns or make predictions based on them. The algorithms learn from the sensor data as they run against it, unlike traditional rules-based analytics systems that follow explicit instructions. Since this involves data preprocessing, we will go through how the MeerKAT telescope data storage infrastructure (called Katstore) manages the voluminous variety, velocity and volume of this data.
RadiaSoft LLC, Boulder, Colorado, USA
LBNL, Berkeley, California, USA
High repetition-rate, ultrafast laser systems play a critical role in a host of modern scientific and industrial applications. We present a diagnostic and correction scheme for controlling and determining laser focal position by utilizing fast wavefront sensor measurements from multiple positions to train a focal position predictor. This predictor and additional control algorithms have been integrated into a unified control interface and FPGA-based controller on beamlines at the Bella facility at LBNL. An optics section is adjusted online to provide the desired correction to the focal position on millisecond timescales by determining corrections for an actuator in a telescope section along the beamline. Our initial proof-of-principle demonstrations leveraged pre-compiled data and pre-trained networks operating ex-situ from the laser system. A framework for generating a low-level hardware description of ML-based correction algorithms on FPGA hardware was coupled directly to the beamline using the AMD Xilinx Vitis AI toolchain in conjunction with deployment scripts. Lastly, we consider the use of remote computing resources, such as the Sirepo scientific framework*, to actively update these correction schemes and deploy models to a production environment.
* M.S. Rakitin et al., "Sirepo: an open-source cloud-based software interface for X-ray source and optics simulations" Journal of Synchrotron Radiation25, 1877-1892 (Nov 2018).
LANL, Los Alamos, New Mexico, USA
This work presents a review of the shift in tuning methods employed at the Los Alamos Neutron Science Center (LANSCE). We explore the tuning categories and methods employed in four key sections of the accelerator, namely the Low-Energy Beam Transport (LEBT), the Drift Tube Linac (DTL), the side-Coupled Cavity Linac (CCL), and the High-Energy Beam Transport (HEBT). The study additionally presents the findings of employing novel software tools and algorithms to enhance each domain’s beam quality and performance. This study showcases the efficacy of integrating model-driven and model-independent tuning techniques, along with acceptance and adaptive tuning strategies, to enhance the optimization of beam delivery to experimental facilities. The research additionally addresses the prospective strategies for augmenting the control system and diagnostics of LANSCE.
*R.W. Garnett, J. Phys.: Conf. Ser. 1021 012001
**A. Scheinker, Rev. ST Accel. Beams 16 102803 2013
***R. Keller, Proc of Part Accel Conf
****M. Oothoudt, Proc of Part Accel Conf, 2003, v4
Fermilab, Batavia, Illinois, USA
Northwestern University, EVANSTON, USA
Northern Illinois University, DeKalb, Illinois, USA