Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator

• URL: http://arxiv.org/abs/2110.12006v1
• Date: Fri, 22 Oct 2021 18:37:22 GMT
• Title: Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator
• Authors: Willem Blokland, Pradeep Ramuhalli, Charles Peters, Yigit Yucesan, Alexander Zhukov, Malachi Schram, Kishansingh Rajput, and Torri Jeske
• Abstract summary: We describe the application of an uncertainty aware Machine Learning method, the Siamese neural network model, to predictupcoming errant beam pulses. By predicting theupcoming failure, we can stop the accelerator before damage occurs.
• Score: 47.187609203210705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the day-to-dayoperations and maximize the delivery of the science, new analytical techniques are being exploredfor anomaly detection, classification, and prognostications. As such, we describe the applicationof an uncertainty aware Machine Learning method, the Siamese neural network model, to predictupcoming errant beam pulses using the data from a single monitoring device. By predicting theupcoming failure, we can stop the accelerator before damage occurs. We describe the acceleratoroperation, related Machine Learning research, the prediction performance required to abort beamwhile maintaining operations, the monitoring device and its data, and the Siamese method andits results. These results show that the researched method can be applied to improve acceleratoroperations.

Related papers

• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• Intelligent Optimization and Machine Learning Algorithms for Structural Anomaly Detection using Seismic Signals [0.9285295512807729]
  The lack of anomaly detection methods during mechanized tunnelling can cause financial loss and deficits in drilling time. On-site excavation requires hard obstacles to be recognized prior to drilling in order to avoid damaging the tunnel boring machine and to adjust the propagation velocity. The efficiency of the structural anomaly detection can be increased with intelligent optimization techniques and machine learning.
  arXiv  Detail & Related papers  (2024-01-18T19:48:53Z)
• Robust Errant Beam Prognostics with Conditional Modeling for Particle Accelerators [36.157808223320465]
  Particle accelerators can fault and abort operations for numerous reasons. These faults impact the availability of particle accelerators during scheduled run-time and hamper the efficiency and the overall science output. We apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability of particle accelerators.
  arXiv  Detail & Related papers  (2023-11-22T07:36:16Z)
• Machine Learning for Quantum-Enhanced Gravitational-Wave Observatories [2.7151296467157184]
  We use machine learning to predict the squeezing level during the third observing run of the Laser Interferometer Gravitational-Wave Observatory. The development of these techniques lays the groundwork for future efforts to optimize squeezed state injection in gravitational-wave detectors.
  arXiv  Detail & Related papers  (2023-05-23T07:47:16Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability [21.98595908296989]
  We demonstrate that the performance of state-of-the-art anomaly detection methods is degraded substantially by adding only small adversarial perturbations to the sensor data. We use different scoring metrics such as prediction errors, anomaly, and classification scores over several public and private datasets. We demonstrate, for the first time, the vulnerabilities of anomaly detection systems against adversarial attacks.
  arXiv  Detail & Related papers  (2022-08-24T01:55:50Z)
• Neuroscience-Inspired Algorithms for the Predictive Maintenance of Manufacturing Systems [0.24999074238880484]
  We propose a method of performing online, real-time anomaly detection for predictive maintenance using Hierarchical Temporal Memory (HTM) Inspired by the human neocortex, HTMs learn and adapt continuously and are robust to noise. Our approach achieves an average score of 64.71, surpassing state-of-the-art deep-learning (49.38) and statistical (61.06) methods.
  arXiv  Detail & Related papers  (2021-02-23T01:31:09Z)
• Adversarial Refinement Network for Human Motion Prediction [61.50462663314644]
  Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend. We propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation.
  arXiv  Detail & Related papers  (2020-11-23T05:42:20Z)
• Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach [68.40429597811071]
  The risk of unplanned equipment downtime can be minimized through Predictive Maintenance of revenue generating assets. A model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context. Unlike traditional black-box regression models, the proposed algorithm self-learns an optimal maintenance policy and provides actionable recommendation for each equipment.
  arXiv  Detail & Related papers  (2020-07-07T10:00:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.