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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Accurate and Fast Anomaly Detection in Additive Composite-Based Manufacturing using Thermal Cameras

Today, large-scale additive manufacturing with plastics and composite materials requires continuous monitoring by experienced staff to prevent, detect and correct anomalous events affecting the performance of the printed part. We address the complexity of this demanding task by designing a camera-based anomaly detection system utilizing probabilistic principal component analysis (PPCA). This is a machine learning technique is trained with thermal images collected during normal operation of the large-scale printer (Cincinnati BAAM). This technique is advantageous for practical applications as there is no need to artificially introduce anomalous conditions into model training. During deployment, we challenge this model by introducing deliberate variations of the extruder speed. We reduce extrusion speed to a lower level, between 70 and 95% of the nominal value to collected test images. Our results show that images are easily identified as anomalous for extruder speeds at or below 85% of the nominal speed, meaning that an anomalous reduction of the material deposition rate can be detected within seconds of its onset. We show that our results are robust to (a) camera-to-camera variability and (b) print-to-print variability.

Pike, John [ORNL]↗
NASA NTRS1987

Probabilistic risk analysis of flying the space shuttle with and without fuel turbine discharge temperature redline protection

An exact mathematical model is presented of the Space Shuttle Main Engine computer voting logic in the presence of High Pressure Fuel Turbine (HPFT) overtemperature events and fuel turbine temperature sensor failures. The model provides estimates of the probability of erroneous engine shutdown and the probability of not detecting a HPFT overtemperature event. Because it is believed that the likelihood of sensor failures and overtemperature events in the HPFT greatly overshadows those in the High Pressure Oxygen Turbine (HPOT), this modeling effort focused on the HPFT. However, because the redline protection logic is the same for both turbines, estimation of the model parameters using relevant HPOT data would provide estimates of the risk of erroneous engine shutdown and an undetected overtemperature event in the HPOT. Because of the complexity of the model, it was necessary to program the solution which thus makes it feasible to accommodate a changing data base. This is considered to be of great interest because of the subjective nature in determining the relevancy of certain failures and the fact that the data base is constantly changing as a result of the frequent engine tests.

Howell, Leonard↗
NASA NTRS1973

Phenology satellite experiment

The author has identified the following significant results. The detection of a phenological event (the Brown Wave-vegetation sensescence) for specific forest and crop types using ERTS-1 imagery is described. Data handling techniques including computer analysis and photointerpretation procedures are explained. Computer analysis of multspectral scanner digital tapes in all bands was used to give the relative changes of spectral reflectance with time of forests and specified crops. These data were obtained for a number of the twenty-four sites located within four north-south corridors across the United States. Analysis of ground observation photography and ERTS-1 imagery for sites in the Appalachian Corridor and Mississippi Valley Corridor indicates that the recession of vegetation development can be detected very well. Tentative conclusions are that specific phenological events such as crop maturity or leaf fall can be mapped for specific sites and possible for different regions. Preliminary analysis based on a number of samples in mixed deciduous hardwood stands indicates that as senescence proceeds both the rate of change and differences in color among species can be detected. The results to data show the feasibility of the development and refinement of phenoclimatic models.

Dethier, B. E.↗
NASA NTRS1998

WSR-88D Cell Trends

This report documents the Applied Meteorology Unit's evaluation of the Cell Trends display as a tool for radar operators to use in their evaluation of storm cell strength. The objective of the evaluation is to assess the utility of the WSR-88D graphical Cell Trends display for local radar cell interpretation in support of the 45th Weather Squadron (45 WS), Spaceflight Meteorology Group (SMG), and National Weather Service (NWS) Melbourne (MLB) operational requirements. The analysis procedure was to identify each cell and track the maximum reflectivity, height of maximum reflectivity, storm top, storm base, hail and severe hail probability, cell-based Vertically Integrated Liquid (VIL) and core aspect ratio using WATADS Build 9.0 cell trends information. One problem noted in the analysis phase was that the Storm Cell Identification and Tracking (SCIT) algorithm had a difficult time tracking the small cells associated with the Florida weather regimes. The analysis indicated numerous occasions when a cell track would end or an existing cell would be give a new ID in the middle of its life cycle. This investigation has found that most cells, which produce hail or microburst events, have discernable Cell Trends signatures. Forecasters should monitor the PUP's Cell Trends display for cells that show rapid (1 scan) changes in both the heights of maximum reflectivity and cell-based VIEL. It is important to note that this a very limited data set (four case days). Fifty-two storm cells were analyzed during those four days. The above mentioned t=ds, increase in the two cell attributes for hail events and decrease in the two cell attributes for wind events were noted in most of the cells. The probability of detection was 88% for both events. The False Alarm Rate (FAR) was a 36% for hail events and a respectable 25% for microburst events. In addition the Heidke Skill Score (HSS) is 0.65 for hail events and 0.67 for microburst events. For random forecast the HSS is 0 and that a perfect score is 1.

Wheeler, Mark M.↗
DOE OSTI2024

Beam-induced backgrounds measured in the ATLAS detector during local gas injection into the LHC beam vacuum

Inelastic beam-gas collisions at the Large Hadron Collider (LHC), within a few hundred metres of the ATLAS experiment, are known to give the dominant contribution to beam backgrounds. These are monitored by ATLAS with a dedicated Beam Conditions Monitor (BCM) and with the rate of fake jets in the calorimeters. These two methods are complementary since the BCM probes backgrounds just around the beam pipe while fake jets are observed at radii of up to several metres. In order to quantify the correlation between the residual gas density in the LHC beam vacuum and the experimental backgrounds recorded by ATLAS, several dedicated tests were performed during LHC Run 2. Local pressure bumps, with a gas density several orders of magnitude higher than during normal operation, were introduced at different locations. The changes of beam-related backgrounds, seen in ATLAS, are correlated with the local pressure variation. In addition the rates of beam-gas events are estimated from the pressure measurements and pressure bump profiles obtained from calculations. Using these rates, the efficiency of the ATLAS beam background monitors to detect beam-gas events is derived as a function of distance from the interaction point. These efficiencies and characteristic distributions of fake jets from the beam backgrounds are found to be in good agreement with results of beam-gas simulations performed with theFluka Monte Carlo programme.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

The Kangaroo’s First Hop: The Early Fast Cooling Phase of EP250108a/SN 2025kg

Fast X-ray transients are a rare and poorly understood population of events. Previously difficult to detect in real time, the launch of the Einstein Probe with its Wide-field X-ray Telescope has led to a rapid expansionof the sample and allowed the exploration of these transients across the electromagnetic spectrum. EP250108a is a recently detected example linked to an optical counterpart, SN 2025kg, or “the kangaroo.” Together with a companion Letter we present our observing campaign and analysis of this event. In this letter, we focus on the early evolution of the optical counterpart over the first 6 days, including our measurement of the redshift of z = 0.17641. We compare to other supernovae and fast transients showing similar features, finding significant similarities with SN 2006aj and SN 2020bvc, and show that the source is well modelled by a rapidly expanding cooling blackbody. We show the observed X-ray and radio properties are consistent with a collapsar-powered jet that is low energy (≲10 51 erg) and/or fails to break out of the dense material surrounding it. While we examine the possibility that the optical emission emerges from the shock produced as the supernova ejecta expand into a dense shell of circumstellar material, due to our X-ray and radio inferences, we favour a model where it arises from a shocked cocoon resulting from a trapped jet. This makes SN 2025 one of the few examples of this currently observationally rare event.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2018

Optimization of Second Fault Detection Thresholds to Maximize Mission POS

In order to support manned spaceflight safety requirements, the Space Launch System (SLS) has defined program-level requirements for key systems to ensure successful operation under single fault conditions. To accommodate this with regards to Navigation, the SLS utilizes an internally redundant Inertial Navigation System (INS) with built-in capability to detect, isolate, and recover from first failure conditions and still maintain adherence to performance requirements. The unit utilizes multiple hardware- and software-level techniques to enable detection, isolation, and recovery from these events in terms of its built-in Fault Detection, Isolation, and Recovery (FDIR) algorithms. Successful operation is defined in terms of sufficient navigation accuracy at insertion while operating under worst case single sensor outages (gyroscope and accelerometer faults at launch). In addition to first fault detection and recovery, the SLS program has also levied requirements relating to the capability of the INS to detect a second fault, tracking any unacceptable uncertainty in knowledge of the vehicle's state. This detection functionality is required in order to feed abort analysis and ensure crew safety. Increases in navigation state error and sensor faults can drive the vehicle outside of its operational as-designed environments and outside of its performance envelope causing loss of mission, or worse, loss of crew. The criteria for operation under second faults allows for a larger set of achievable missions in terms of potential fault conditions, due to the INS operating at the edge of its capability. As this performance is defined and controlled at the vehicle level, it allows for the use of system level margins to increase probability of mission success on the operational edges of the design space. Due to the implications of the vehicle response to abort conditions (such as a potentially failed INS), it is important to consider a wide range of failure scenarios in terms of both magnitude and time. As such, the Navigation team is taking advantage of the INS's capability to schedule and change fault detection thresholds in flight. These values are optimized along a nominal trajectory in order to maximize probability of mission success, and reducing the probability of false positives (defined as when the INS would report a second fault condition resulting in loss of mission, but the vehicle would still meet insertion requirements within system-level margins). This paper will describe an optimization approach using Genetic Algorithms to tune the threshold parameters to maximize vehicle resilience to second fault events as a function of potential fault magnitude and time of fault over an ascent mission profile. The analysis approach, and performance assessment of the results will be presented to demonstrate the applicability of this process to second fault detection to maximize mission probability of success.

Anzalone, Evan↗
NASA NTRS1973

Phenology satellite experiment

The detection of a phenological event (the brown wave-vegetation senescence) for specific forest and crop types using ERTS-1 imagery is described. Data handling techniques included computer analysis and photo interpretation procedures. Computer analysis of ERTS-1 multispectral scanner digital tapes in all bands was used to give the relative changes of spectral reflectance with time of forests and specified crops. These data were obtained for a number of the study's twenty-four sites located within four north-south corridors across the United States. Analysis of ground observation photography and ERTS-1 imagery for sites in the Appalachian Corridor and Mississippi Valley Corridor indicates that the recession of vegetation development can be detected very well. Tentative conclusions are that specific phenological events such as crop maturity or leaf fall can be mapped for specific sites and possibly for entire regions.

Dethier, B. E.↗
NASA NTRS2013

Estimation of Rain Intensity Spectra over the Continental US Using Ground Radar-Gauge Measurements

A high-resolution surface rainfall product is used to estimate rain characteristics over the continental US as a function of rain intensity. By defining each data at 4-km horizontal resolutions and 1-h temporal resolutions as an individual precipitating/nonprecipitating sample, statistics of rain occurrence and rain volume including their geographical and seasonal variations are documented. Quantitative estimations are also conducted to evaluate the impact of missing light rain events due to satellite sensors' detection capabilities. It is found that statistics of rain characteristics have large seasonal and geographical variations across the continental US. Although heavy rain events (> 10 mm/hr.) only occupy 2.6% of total rain occurrence, they may contribute to 27% of total rain volume. Light rain events (< 1.0 mm/hr.), occurring much more frequently (65%) than heavy rain events, can also make important contributions (15%) to the total rain volume. For minimum detectable rain rates setting at 0.5 and 0.2 mm/hr which are close to sensitivities of the current and future space-borne precipitation radars, there are about 43% and 11% of total rain occurrence below these thresholds, and they respectively represent 7% and 0.8% of total rain volume. For passive microwave sensors with their rain pixel sizes ranging from 14 to 16 km and the minimum detectable rain rates around 1 mm/hr., the missed light rain events may account for 70% of train occurrence and 16% of rain volume. Statistics of rain characteristics are also examined on domains with different temporal and spatial resolutions. Current issues in estimates of rain characteristics from satellite measurements and model outputs are discussed.

Lin, Xin↗
DOE OSTI2020

Non-routine adjustments – towards standardizing M&V approach for quantifying the effects of static factors

The success of an energy management program relies on ensuring that the energy savings can be verified with an adequate level of certainty. Energy savings cannot be directly measured and hence have to be deduced by comparing the pre-retrofit energy consumption with post-retrofit energy consumption data adjusted to account for differences in conditions. These adjustments are needed, since conditions that influence energy consumption may not stay the same between pre- and postinstallation phase of the project. These adjustments can often be routine when accounting for factors like production volume and weather that are expected to change and are included in the energy consumption adjustment model. Existing measurement and verification (M&V) resources and guidance mostly concentrate on developing models for routine adjustment of one or more factors that normally change. On the other hand, there are factors (static) like product mix, operating hours, gross square area, etc. that are assumed to stay constant during normal conditions. However, in order to adapt to dynamic market conditions, the industries are forced to react thereby leading to changes to these static factors. Identifying these static factors that warrant adjustment, called non-routine event, along with quantifying their effect on energy consumption can be complex and lack of proper guidance can exacerbate this issue. This paper reviews some of the current work in terms of how non-routine events are defined, characterized, detected and quantified based on a review of existing M&V guidelines, protocols and other relevant literature. This work also reviewed some of the existing non-routine events and adjustment practices to understand how these different aspects are addressed along with a discussion on some of the key challenges and gaps in the current guidance.

Earni, Shankar↗
NASA NTRS1993

Correlated variations of UV and radio emissions during an outstanding Jovian auroral event

An exceptional Jovian aurora was detected in the FUV on December 21, 1990, by means of Vilspa and Goddard Space Flight Center (GFSC) International Ultraviolet Explorer (IUE) observations. This event included intensification by a factor of three between December 20 and 21, leading to the brightest aurora identified in the IUE data analyzed, and, in the north, to a shift of the emission peak towards larger longitudes. The Jovian radio emission simultaneously recorded at decameter wavelengths in Nancay also exhibits significant changes, from a weak and short-duration emission on December 20 to a very intense one, lasting several hours, on December 21. Confirmation of this intense radio event is also found in the observations at the University of Florida on December 21. The emissions are identified as right-handed Io-independent 'A' (or 'non Io-A') components from the northern hemisphere. The radio source region deduced from the Nancay observations lies, for both days, close to the UV peak emission, exhibiting in particular a similar shift of the source region toward larger longitudes from one day to the next. A significant broadening of the radio source was also observed and it is shown that on both days, the extent of the radio source closely followed the longitude range for which the UV brightness exceeds a given threshold. The correlated variations, both in intensity and longitude, strongly suggest that a common cause triggered the variation of the UV and radio emissions during this exceptional event. On one hand, the variation of the UV aurora could possibly be interpreted according to the Prange and Elkhamsi (1991) model of diffuse multicomponent auroral precipitation (electron and ion): it would arise from an increase in the precipitation rate of ions together with an inward shift of their precipitation locus from L approximately equal 10 to L approximately equal 6. On the other hand, the analysis of Ulysses observations in the upstream solar wind suggests that a significant disturbance in the solar wind, involving the generation of an interplanetary shock and the presence of a CME have interacted with the Jovian magnetosphere at about the time of the auroral event. Both arguments suggest that we may have observed for the first time a magnetic storm-type interaction in an outer planet magnetosphere, affecting simultaneously several auroral processes. Conversely, the observed relationship between the level of UV auroral activity and the detection of decameter emission (DAM), if it were a typical feature, might argue in favour of a more direct and permanent association between the auroral processes leading to UV and radio aurorae, possibly related to 'discrete-arc'-like activity and electron precipitation.

Prange, R.↗
NASA NTRS2003

Propulsion Health Monitoring for Enhanced Safety

This report presents the results of the NASA contract Propulsion System Health Management for Enhanced Safety performed by General Electric Aircraft Engines (GE AE), General Electric Global Research (GE GR), and Pennsylvania State University Applied Research Laboratory (PSU ARL) under the NASA Aviation Safety Program. This activity supports the overall goal of enhanced civil aviation safety through a reduction in the occurrence of safety-significant propulsion system malfunctions. Specific objectives are to develop and demonstrate vibration diagnostics techniques for the on-line detection of turbine rotor disk cracks, and model-based fault tolerant control techniques for the prevention and mitigation of in-flight engine shutdown, surge/stall, and flameout events. The disk crack detection work was performed by GE GR which focused on a radial-mode vibration monitoring technique, and PSU ARL which focused on a torsional-mode vibration monitoring technique. GE AE performed the Model-Based Fault Tolerant Control work which focused on the development of analytical techniques for detecting, isolating, and accommodating gas-path faults.

Butz, Mark G.↗
NASA NTRS2018

Geostationary Lightning Mapper On-Orbit Sources of False Events

The first Geostationary Lightning Mapper (GLM) was launched aboard the GOES-R Spacecraft (now GOES-East) on November 19, 2016 and is now fully operational. GLM uses a high-speed camera and onboard video processing to detect the optical emission of lightning for the full disk observed from the geostationary orbit. During the Post Launch Test period the instrument and ground processing algorithms (GPAs) were tuned to optimize the tradeoff between detection efficiency and false event rate. False events, those not due to lightning, arise from a variety of sources. A primary function of the GPAs is to remove false events prior to assembling events into groups and flashes for use by weather forecasters and scientists. Effective discrimination of false events depends on understanding the phenomenology of the various sources of false events.

GOES-R↗
DOE OSTI2023

Jetted and Turbulent Stellar Deaths: New LVK-detectable Gravitational-wave Sources

Upcoming LIGO–Virgo–KAGRA (LVK) observing runs are expected to detect a variety of inspiralling gravitational-wave (GW) events that come from black hole and neutron star binary mergers. Detection of noninspiral GW sources is also anticipated. We report the discovery of a new class of noninspiral GW sources—the end states of massive stars—that can produce the brightest simulated stochastic GW burst signal in the LVK bands known to date, and could be detectable in LVK run A+. Some dying massive stars launch bipolar relativistic jets, which inflate a turbulent energetic bubble—cocoon—inside of the star. We simulate such a system using state-of-the-art 3D general relativistic magnetohydrodynamic simulations and show that these cocoons emit quasi-isotropic GW emission in the LVK band, ~10–100 Hz, over a characteristic jet activity timescale ~10–100 s. Our first-principles simulations show that jets exhibit a wobbling behavior, in which case cocoon-powered GWs might be detected already in LVK run A+, but it is more likely that these GWs will be detected by the third-generation GW detectors with an estimated rate of ~10 events yr –1 . The detection rate drops to ~1% of that value if all jets were to feature a traditional axisymmetric structure instead of a wobble. Accompanied by electromagnetic emission from the energetic core-collapse supernova and the cocoon, we predict that collapsars are powerful multimessenger events.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Electrical Fault Detection, Power Quality, Distributed Energy Resource Use Cases, and Cyber Event Applications with the Cyber Grid Guard System Using Distributed Ledger Technology

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation and are associated with distributed energy resources (DERs). The integrity and confidentiality of data from IEDs is crucial, and distributed ledger technology (DLT) could improve the resilience of microgrids by helping to make these data more secure. The most popular applications using blockchain technology for electrical utilities is in the field is based on energy trading. However, the dynamism of the penetration of customer owned DERs and the deployment of sensors with IEDs have led to the identification of new applications using DLT that are focused on other areas, such as monitoring, operation and management of the grid and its assets. In addition, the majority of studies on electrical grid applications with blockchain were validated with software simulations. Although general monitoring of power systems for using DLT could be evaluated in operational electric grids, other DLT research applications such as defense against cyber-attacks and/or electrical fault detection are not likely to be performed in a real infrastructure because of possible risks to the network/equipment security. This report summarizes the application of power system applications using distributed ledger technology (DLT), providing a secure DLT framework for collecting data from IEDs like power meters and protective relays inside and outside of an electrical substation and/or between two different electrical utilities. In this study, the use case scenarios were created and assessed for different power system application by using DLT. The electrical fault detection for faulted phases (1), power quality monitoring of phase voltage magnitudes, frequency levels and load power factor (2), DERs use case monitoring (3), and cyber-event applications (4) were performed in a test bed with a Cyber-Grid Guard (CGG) system using DLT. It had a real-time simulator with power meters and protective relays in-the-loop. The first section of this report presents a literature review of power system applications using blockchain at research level. The second section shows the theory and equations used on this report. The third section shows the description of the test bed, equipment, architecture, and electrical grid diagrams. The fourth section shows the experimental models and use case scenarios that were performed for the electrical fault detection, power quality, DERs use case, and cyber event applications with the CGG system using DLT. The fifth section shows the results collected from the tests based on comparing the time stamped events of the analog signals from the IEDs, DLT computer and real time simulator. The sixth section performed the discussion of the results for the use case scenarios. Finally, section seven presents the conclusions for this report were presented.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Evaluating Ensemble Predictions of South Asian Monsoon Low Pressure System Genesis

Abstract Synoptic-scale vortices known as monsoon low pressure systems (LPSs) frequently produce intense precipitation and hydrological disasters in South Asia, so accurately forecasting LPS genesis is crucial for improving disaster preparedness and response. However, the accuracy of LPS genesis forecasts by numerical weather prediction models has remained unknown. Here, we evaluate the performance of two global ensemble models—the U.S. Global Ensemble Forecast System (GEFS) and the Ensemble Prediction System of the European Centre for Medium-Range Weather Forecasts (ECMWF)—in predicting LPS genesis during the years 2021–22. The GEFS successfully predicted about half the observed LPS genesis events 1–2 days in advance; the ECMWF model captured an additional 10% of observed genesis events. Both models had a false alarm ratio (FAR) of around 50% for 1–2-day lead times. In both ensembles, the control run typically exhibited a higher probability of detection (POD) of observed events and a lower FAR compared to the perturbed ensemble members. However, a consensus forecast, in which genesis is predicted when at least 20% of ensemble members forecast LPS formation, had POD values surpassing those of the control run for all lead times. Moreover, probabilistic predictions of genesis over the Bay of Bengal, where most LPSs form, were skillful, with the fraction of ensemble members predicting LPS formation over a 5-day lead time approximating the observed frequency of genesis, without any adjustment or bias correction.

Suhas, D. L.↗
DOE OSTI2021

Measurements of Argon-39 from locations near historic underground nuclear explosions

Measurement of radioactive gas seepage from an underground nuclear explosion is one of the primary methods to confirm whether an event was nuclear in nature. Radioactive noble gas indicators that are commonly targeted by such measurements (e.g. 133Xe, 37Ar) have half-lives of 35 days or less. Argon-39, an activation product similar to 37Ar, is produced by the interaction between neutrons and potassium in the surrounding geology and has a half-life of 269 years. Measurements taken at three sites near three historic underground nuclear test locations at the Nevada National Security Site have all shown highly elevated levels of 39Ar in subsurface air decades after the test events. Elevated levels of 39Ar (30-50 times background) were also detected in atmospheric air collected roughly 30 cm above ground level near two of these sites, and outside the entrance of the one tunnel site. These measurements demonstrate that 39Ar has the potential to be a long-term signature of an underground nuclear event which can be reliably detected at the surface or in the shallow subsurface. This radionuclide detection of an underground nuclear event decades after the event takes place is in contrast to the commonly held assumption that detecting underground nuclear events via radionuclides at the surface needs to be done in a matter of months. Depending upon what further studies show about the robustness of this signature in a variety of geological settings, it may in fact be easy to detect underground nuclear events at the surface for a very long time post-detonation.

Johnson, Christine M.↗
NASA NTRS2011

Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses adaptive eigenbases to combine 1) prior knowledge about uninteresting signals with 2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply the method to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies.

anomaly detection↗