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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Automated defect detection in spent nuclear fuel using combine Cerenkov and Gamma Emission Tomography data

Spent fuel monitoring and characterization has been a pillar of safeguards and nuclear facility monitoring for many years. The Digital Cerenkov Viewing Device (DCVD) has been used since the 1980s as a method of defect detection in spent fuel. In recent years, the accounting of large quantities of spent fuel before storage has renewed interest in this relatively quick and inexpensive method. This has an impact not only in safeguards, but also in for nuclear power facilities, as accounting can be a long and arduous process. Additionally, the DCVD demonstrates limited accuracy in more complex cases such as substitution of a fuel rod with steel, or a partial defect detection. A second method, Gamma Emission Tomography (GET) has been explored as an improved defect detection method, but is much more expensive and invasive than DCVD. This work identifies deficiencies in both methods, and proposes a combination of data gathered from each method to address these deficiencies for improved spent fuel characterization. Initial results are promising, showing 97% detection of a single missing fuel rod when the data types are combined, versus approximately 50% and 70%respectively for DCVD and GET data on their own. Finally, these classification results are obtained with algorithms derived from facial recognition and applied to this problem, yielding unique accuracy in near real-time while also maintaining the information barrier between output and measurement desired in safeguards.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Pattern Mining and Anomaly Detection based on the Power System Synchrophasor Measurements

Real-time monitoring of power system dynamics using phasor measurement units (PMUs) data improves situational awareness and system reliability and potentially can help prevent blackouts due to early anomaly detection. The study presented in the paper is based on the actual PMU measurements of the U.S. western interconnection system. Given the nonlinear and nonstationary PMUs data, we developed a robust anomaly detection framework. Wavelet-based multi-resolution analysis, combined with moving-window-based outlier detection and anomaly scoring, were deployed to identify potential PMU events. The candidate events were evaluated using spatiotemporal correlation analysis, and then classified for a better understanding of the event types. The results demonstrated successful anomaly detection and classification, compared with the recorded real-world events.

Ren, Huiying↗
DOE OSTI2016

THE DETECTION RATE OF EARLY UV EMISSION FROM SUPERNOVAE: A DEDICATED GALEX/PTF SURVEY AND CALIBRATED THEORETICAL ESTIMATES

The radius and surface composition of an exploding massive star, as well as the explosion energy per unit mass, can be measured using early UV observations of core-collapse supernovae (SNe). We present the first results from a simultaneous GALEX/PTF search for early ultraviolet (UV) emission from SNe. Six SNe II and one Type II superluminous SN (SLSN-II) are clearly detected in the GALEX near-UV (NUV) data. We compare our detection rate with theoretical estimates based on early, shock-cooling UV light curves calculated from models that fit existing Swift and GALEX observations well, combined with volumetric SN rates. We find that our observations are in good agreement with calculated rates assuming that red supergiants (RSGs) explode with fiducial radii of 500 R{sub ⊙}, explosion energies of 10{sup 51} erg, and ejecta masses of 10 M{sub ⊙}. Exploding blue supergiants and Wolf–Rayet stars are poorly constrained. We describe how such observations can be used to derive the progenitor radius, surface composition, and explosion energy per unit mass of such SN events, and we demonstrate why UV observations are critical for such measurements. We use the fiducial RSG parameters to estimate the detection rate of SNe during the shock-cooling phase (<1 day after explosion) for several ground-based surveys (PTF, ZTF, and LSST). We show that the proposed wide-field UV explorer ULTRASAT mission is expected to find >85 SNe per year (∼0.5 SN per deg{sup 2}), independent of host galaxy extinction, down to an NUV detection limit of 21.5 mag AB. Our pilot GALEX/PTF project thus convincingly demonstrates that a dedicated, systematic SN survey at the NUV band is a compelling method to study how massive stars end their life.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1985

The application of the detection filter to aircraft control surface and actuator failure detection and isolation

The performance of the detection filter in detecting and isolating aircraft control surface and actuator failures is evaluated. The basic detection filter theory assumption of no direct input-output coupling is violated in this application due to the use of acceleration measurements for detecting and isolating failures. With this coupling, residuals produced by control surface failures may only be constrained to a known plane rather than to a single direction. A detection filter design with such planar failure signatures is presented, with the design issues briefly addressed. In addition, a modification to constrain the residual to a single known direction even with direct input-output coupling is also presented. Both the detection filter and the modification are tested using a nonlinear aircraft simulation. While no thresholds were selected, both filters demonstrated an ability to detect control surface and actuator failures. Failure isolation may be a problem if there are several control surfaces which produce similar effects on the aircraft. In addition, the detection filter was sensitive to wind turbulence and modeling errors.

Bonnice, W. F.↗
NASA NTRS1994

In situ detection of tropospheric OH and HO2 by laser-induced fluorescence in a detection chamber at low pressure

Just as in the method of Hard and O'Brien, ambient air is pulled through an approximately 1 mm diameter inlet into a detection chamber that is maintained at a pressure of 2.4 torr. The ambient air stream travels through the detection chamber with a velocity of greater than 100 m sec(exp -1) in a narrow stream, constrained by the addition of an inert gas flow (0.4 torr). The OH molecule is both excited and detected in the A(exp 2)Sigma (v' = 0) yields X(exp 2)II (v'' = 0) transition at 308 nm. Light from a copper vapor-pumped dye laser (rep. rate = 10kHz; pulse length = 20 ns; linewidth = .1 cm(exp -1), and average power = 15 mW), resonant with the Q(sub 1)(3) transition, is multipassed through a White cell and intercepts the air stream as 24 non-overlapping 2mm by 5mm beams. A fast microchannel plate detector is turned off during the laser pulse to prevent saturation of the detector due to Rayleigh and chamber scattering. It is turned on 30-100 nsec after the end of the laser pulse for 300 ns to collect resonance fluorescence from OH. HO2 is detected by chemical conversion to OH by reaction with reagent NO, followed by OH detection. Both the detection sensitivity and the inlet characteristics must be understood for any in situ instrument. For the calibration of the detection sensitivity, OH is produced quantitatively by the fast H + NO2 yields OH + NO reaction in a low pressure, flowing discharge tube connected to the detection chamber. The inlet transmission of OH inlet is calibrated separately.

Brune, William H.↗
DOE OSTI2020

Target Detection via Cognitive Radars Using Change-Point Detection, Learning, and Adaptation

Many radar detection algorithms that assume a stationary environment (clutter) have been proposed and analyzed over the years. However, in practice, changes in the nonstationary environment can perturb the parameters of the clutter distribution, or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which are required to rapidly realize the nonstationarity, accurately learn the new characteristics of the environments, and adaptively update the detector. In this paper, aiming to develop a fully cognitive radar for target detection in nonstationary environments, we propose a unifying framework that integrates (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by applying sparse theory and kernel density estimation methods, and (iii) adaptive target detection by automatically modifying the likelihood-ratio test and corresponding detection threshold. Further, with extensive numerical examples, we demonstrate the achieved improvements in detection performance due to the proposed framework in comparison to a nonadaptive case, an adaptive matched filter (AMF) method, and the clairvoyant case. Herein, we also use Wilcoxon rank-sum tests to evaluate the statistical significance of the performance improvements

42 ENGINEERING↗
DOE OSTI2020

Distributed Detection of Malicious Attacks on Consensus Algorithms with Applications in Power Networks

Consensus-based distributed algorithms are well suited for coordination among agents in a cyber-physical system. These distributed schemes, however, suffer from their vulnerability to cyber attacks that are aimed at manipulating data and control ow. In this article, we present a novel distributed method for detecting the presence of such intrusions for a distributed multi-agent system following ratio consensus. We employ a Max-Min protocol to develop low cost, easy to implement detection strategies where each participating node detects the intrusion independently, eliminating the need for a trusted certifying agent in the network. The effectiveness of the detection method is demonstrated by numerical simulations on a 1000 node network to demonstrate the efficacy and simplicity of implementation.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2016

Improvements in the Method of Radiation Anomaly Detection by Spectral Comparison Ratios

Algorithms for detecting illicit gamma radiation sources must respond to a wide variety of threat signatures and ignore a wider variety of clutter. Previously, we described the Nuisance-rejection Spectral Comparison Ratio Anomaly Detection (N-SCRAD) method for indicating the presence of threat sources during mobile search operations. Our method relies on the formulation of radiation alarm test statistics applied to detector counts in numerous spectral regions of interest and our test statistics are standardized by local estimates of background variability. In this work we present a new automated procedure for selecting test statistics and specifying the regions of interest based on minimizing detectable count rates of prototype threat spectra at a specified false positive rate using simulated annealing. The new procedure can be applied to configuring the detection algorithm when the prototype threats are many and diverse without significantly compromising the detection sensitivity to any one of them. N-SCRAD makes use of estimates of background variability obtained during a search from prior measurements of background. In applications such estimates made purely from past measurements sometimes do not extrapolate well to current conditions. In this work we present a new method for correcting those estimates to current conditions of the total count rate. We demonstrate that the correction lowers detection thresholds for a specified false positive rate, enabling greater sensitivity to targets. The two proposed improvements to N-SCRAD maximize detection power for the expected variety of possible threats while reducing or maintaining the false positive rate.

Anomaly detection, Gamma-ray spectroscopy, Radiati↗
DOE OSTI2016

Optimization of scat detection methods for a social ungulate, the wild pig, and experimental evaluation of factors affecting detection of scat

Collection of scat samples is common in wildlife research, particularly for genetic capture-mark-recapture applications. Due to high degradation rates of genetic material in scat, large numbers of samples must be collected to generate robust estimates. Optimization of sampling approaches to account for taxa-specific patterns of scat deposition is, therefore, necessary to ensure sufficient sample collection. While scat collection methods have been widely studied in carnivores, research to maximize scat collection and noninvasive sampling efficiency for social ungulates is lacking. Further, environmental factors or scat morphology may influence detection of scat by observers. We contrasted performance of novel radial search protocols with existing adaptive cluster sampling protocols to quantify differences in observed amounts of wild pig ( Sus scrofa ) scat. We also evaluated the effects of environmental (percentage of vegetative ground cover and occurrence of rain immediately prior to sampling) and scat characteristics (fecal pellet size and number) on the detectability of scat by observers. We found that 15- and 20-m radial search protocols resulted in greater numbers of scats encountered than the previously used adaptive cluster sampling approach across habitat types, and that fecal pellet size, number of fecal pellets, percent vegetative ground cover, and recent rain events were significant predictors of scat detection. Our results suggest that use of a fixed-width radial search protocol may increase the number of scats detected for wild pigs, or other social ungulates, allowing more robust estimation of population metrics using noninvasive genetic sampling methods. Further, as fecal pellet size affected scat detection, juvenile or smaller-sized animals may be less detectable than adult or large animals, which could introduce bias into abundance estimates. In conclusion, knowledge of relationships between environmental variables and scat detection may allow researchers to optimize sampling protocols to maximize utility of noninvasive sampling for wild pigs and other social ungulates.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

This work is an extension of a recently developed software tool called MILD (Multi-level Immune Learning Detection), which implements a negative selection algorithm for anomaly and fault detection that is inspired by the human immune system. The immunity-based approach can detect a broad spectrum of known and unforeseen faults. We extend MILD by applying a neural network classifier to identify the pattern of fault detectors that are activated during fault detection. Consequently, MILD now performs fault detection and identification of the system under investigation. This paper describes the application of MILD to detect and classify faults of a generic transport aircraft augmented with an intelligent flight controller. The intelligent control architecture is designed to accommodate faults without the need to explicitly identify them. Adding knowledge about the existence and type of a fault will improve the handling qualities of a degraded aircraft and impact tactical and strategic maneuvering decisions. In addition, providing fault information to the pilot is important for maintaining situational awareness so that he can avoid performing an action that might lead to unexpected behavior - e.g., an action that exceeds the remaining control authority of the damaged aircraft. We discuss the detection and classification results of simulated failures of the aircraft's control system and show that MILD is effective at determining the problem with low false alarm and misclassification rates.

Wong, Derek↗
DOE OSTI2021

Individual Wave Detection and Tracking within a Rotating Detonation Engine through Computer Vision Object Detection applied to High-Speed Images

Known for their simplistic design and continuous detonation, rotating detonation engines (RDEs) constitute a majority of current pressure gain combustion (PGC) research efforts. Experimental RDE operation times have been continuously extended through the use of rig cooling techniques. As the window of observable behavior is expanded, and as the technology matures toward eventual integration within gas turbines, monitoring techniques must evolve to better match industrial diagnostics. High-speed image analysis techniques prove useful to capture and evaluate the unsteady detonation behavior within the RDE. Traditional image analysis techniques, however, require extensive processing times which prohibit simultaneous monitoring. To better address this problem, a computer vision object detection methodology is proposed to quickly detect individual detonation waves within a single down-axis image. Detonation waves are detected in individual images by the implemented computer vision method You Only Look Once (YOLO) object detection network. In order to detect detonation waves, the network must first be trained using RDE images of interest, for which each required phase of network development is outlined. Detection of waves is improved through proper treatment of the collected image set, variation of Intersection over Union (IoU) and confidence thresholding, and through a parametric study of annotation dimensions. Each detected wave is described by its location and rotational direction, and locations are tracked to calculate wave velocity across each frame, leading to a timestep resolution of 20 µs. Wave velocities are also calculated through a series of frames, leading to a suitable average velocity estimation using as few as 10 frames. Uncertainty analysis accounting for variation in camera framerate, pixel width and annotation centroid locations estimates a total uncertainty of ±4.3% for velocity calculations, using the smallest annotation boxes. This method offers great reductions in processing times, as a step toward real-time monitoring of detonation waves within an RDE. Improving on previous studies, this technique is impartial to wave modes not included in the original training set and calculates wave velocities independent of high-speed pressure data. The ability to isolate waves within predicted bounding boxes will likely facilitate analysis of pixel intensity variation as an estimation of wave strength in future work.

Johnson, Kristyn↗
DOE OSTI2022

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Abstract Fault detection and classification in photovoltaic (PV) systems through real‐time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend‐based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change‐point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

14 SOLAR ENERGY↗
DOE OSTI2015

Global limits and interference patterns in dark matter direct detection

We compare the general effective theory of one-body dark matter nucleon interactions to current direct detection experiments in a global multidimensional statistical analysis. We derive exclusion limits on the 28 isoscalar and isovector coupling constants of the theory, and show that current data place interesting constraints on dark matter-nucleon interaction operators usually neglected in this context. We characterize the interference patterns that can arise in dark matter direct detection from pairs of dark matter-nucleon interaction operators, or from isoscalar and isovector components of the same operator. We find that commonly neglected destructive interference effects weaken standard direct detection exclusion limits by up to one order of magnitude in the coupling constants.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

THE DETECTION OF A SN IIn IN OPTICAL FOLLOW-UP OBSERVATIONS OF ICECUBE NEUTRINO EVENTS

The IceCube neutrino observatory pursues a follow-up program selecting interesting neutrino events in real-time and issuing alerts for electromagnetic follow-up observations. In 2012 March, the most significant neutrino alert during the first three years of operation was issued by IceCube. In the follow-up observations performed by the Palomar Transient Factory (PTF), a Type IIn supernova (SN IIn) PTF12csy was found 0.°2 away from the neutrino alert direction, with an error radius of 0.°54. It has a redshift of z = 0.0684, corresponding to a luminosity distance of about 300 Mpc and the Pan-STARRS1 survey shows that its explosion time was at least 158 days (in host galaxy rest frame) before the neutrino alert, so that a causal connection is unlikely. The a posteriori significance of the chance detection of both the neutrinos and the SN at any epoch is 2.2σ within IceCube's 2011/12 data acquisition season. Also, a complementary neutrino analysis reveals no long-term signal over the course of one year. Therefore, we consider the SN detection coincidental and the neutrinos uncorrelated to the SN. However, the SN is unusual and interesting by itself: it is luminous and energetic, bearing strong resemblance to the SN IIn 2010jl, and shows signs of interaction of the SN ejecta with a dense circumstellar medium. High-energy neutrino emission is expected in models of diffusive shock acceleration, but at a low, non-detectable level for this specific SN. In this paper, we describe the SN PTF12csy and present both the neutrino and electromagnetic data, as well as their analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2017

Kepler Planet Detection Metrics: Per-Target Detection Contours for Data Release 25

A necessary input to planet occurrence calculations is an accurate model for the pipeline completeness (Burke et al., 2015). This document describes the use of the Kepler planet occurrence rate products in order to calculate a per-target detection contour for the measured Data Release 25 (DR25) pipeline performance. A per-target detection contour measures for a given combination of orbital period, Porb, and planet radius, Rp, what fraction of transit signals are recoverable by the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017). The steps for calculating a detection contour follow the procedure outlined in Burke et al. (2015), but have been updated to provide improved accuracy enabled by the substantially larger database of transit injection and recovery tests that were performed on the final version (i.e., SOC 9.3) of the Kepler pipeline (Christiansen, 2017; Burke Catanzarite, 2017a). In the following sections, we describe the main inputs to the per-target detection contour and provide a worked example of the python software released with this document (Kepler Planet Occurrence Rate Tools KeplerPORTs)1 that illustrates the generation of a detection contour in practice. As background material for this document and its nomenclature, we recommend the reader be familiar with the previous method of calculating a detection contour (Section 2 of Burke et al.,2015), input parameters relevant for describing the data quantity and quality of Kepler targets (Burke Catanzarite, 2017b), and the extensive new transit injection and recovery tests of the Kepler pipeline (Christiansen et al., 2016; Burke Catanzarite, 2017a; Christiansen, 2017).

Planet Detection Metrics↗