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NASA NTRS2017

Seasonal Clear-Sky Flux and Cloud Radiative Effect Anomalies in the Arctic Atmospheric Column Associated with the Arctic Oscillation and Arctic Dipole

The impact of the Arctic Oscillation (AO) and Arctic Dipole (AD) on the radiative flux into the Arctic mean atmospheric column is quantified. 3-month-averaged AO and AD indices are regressed with corresponding surface and top-of-atmosphere (TOA) fluxes from the CERES-SFC and CERES-TOA EBAF datasets over the period 2000-2014. An increase in clear-sky fluxes into the Arctic mean atmospheric column during fall is the largest net flux anomaly associated with AO, primarily driven by a positive net longwave flux anomaly (i.e. increase of net flux into the atmospheric column) at the surface. A decrease in the Arctic mean atmospheric column cloud radiative effect during winter and spring is the largest flux anomaly associated with AD, primarily driven by a change in the longwave cloud radiative effect at the surface. These prominent responses to AO and AD are widely distributed across the ice-covered Arctic, suggesting that the physical process or processes that bring about the flux change associated with AO and AD are distributed throughout the Arctic.

Hegyi, Bradley M.↗

NASA NTRS2017

Utilizing Stable Isotopes and Isotopic Anomalies to Study Early Solar System Formation Processes

Chondritic meteorites contain a diversity of particle components, i.e., chondrules and calcium-, aluminum-rich refractory inclusions (CAIs), that have survived since the formation of the Solar System. The chemical and isotopic compositions of these materials provide a record of the conditions present in the protoplanetary disk where they formed and can aid our understanding of the processes and reservoirs in which solids formed in the solar nebula, an important step leading to the accretion of planetesimals. Isotopic anomalies associated with nucleosynthetic processes are observed in these discrete materials, and can be compared to astronomical observations and astrophysical formation models of stars and more recently proplyds. The existence and size of these isotopic anomalies are typically thought to reflect a significant state of isotopic heterogeneity in the earliest Solar System, likely left over from molecular cloud heterogeneities on the grain scale, but some could also be due to late stellar injection. The homogenization of these isotopic anomalies towards planetary values can be used to track the efficiency and timescales of disk wide mixing,

Simon, Justin↗

NASA NTRS2016

Advanced Stirling Radioisotope Generator EU2 Anomaly Investigation

The Advanced Stirling Radioisotope Generator (ASRG) Engineering Unit 2 (EU2) is the highest fidelity electrically-heated Stirling radioisotope generator built to date. NASA Glenn Research Center (GRC) completed the assembly of the ASRG EU2 in September, 2014 using hardware from the now cancelled ASRG flight development project. The ASRG EU2 integrated the first pair of Sunpower's ASC-E3 Stirling convertors (ASC-E3 #1 and #2) in an aluminum generator housing with Lockheed Martin's Engineering Development Unit (EDU) 4 controller. After just 179 hours of EU2 generator operation, the first power fluctuation occurred on ASC-E3 #1. The first power fluctuation occurred 175 hours later on ASC-E3 #2. Over time, the power fluctuations became more frequent on both convertors and larger in magnitude. Eventually the EU2 was shut down in January, 2015. An anomaly investigation was chartered to determine root cause of the power fluctuations and other anomalous observations. A team with members from GRC, Sunpower, and Lockheed Martin conducted a thorough investigation of the EU2 anomalies. Findings from the EU2 disassembly identified proximate causes of the anomalous observations. Discussion of the team's assessment of the primary possible failure theories, root cause, and conclusions is provided. Recommendations are made for future Stirling generator development to address the findings from the anomaly investigation. Additional findings from the investigation are also discussed.

Lewandowski, Edward J.↗

NASA NTRS2018

Advanced Stirling Radioisotope Generator Engineering Unit 2 Anomaly Investigation

The Advanced Stirling Radioisotope Generator (ASRG) Engineering Unit 2 (EU2) is the highest fidelity electrically heated Stirling radioisotope generator built to date. NASA Glenn Research Center completed the assembly of the ASRG EU2 in September 2014 using hardware from the now cancelled ASRG flight development project. The ASRG EU2 integrated the first pair of Sunpower's Advanced Stirling Convertors (ASC−E3 #1 and #2) in an aluminum generator housing with Lockheed Martin's (LM's) Engineering Development Unit (EDU) 4 controller. After just 179 hr of EU2 generator operation, the first power fluctuation occurred on ASC−E3 #1. The first power fluctuation occurred 175 hr later on ASC−E3 #2. Over time, the power fluctuations became more frequent on both convertors and larger in magnitude. Eventually the EU2 was shut down in January 2015. An anomaly investigation was chartered to determine root cause of the power fluctuations and other anomalous observations. A team with members from Glenn, Sunpower, and LM conducted a thorough investigation of the EU2 anomalies. Findings from the EU2 disassembly identified proximate causes of the anomalous observations. Discussion of the team's assessment of the primary possible failure theories, root cause, and conclusions is provided. Recommendations are made for future Stirling generator development to address the findings from the anomaly investigation. Additional findings from the investigation are also discussed.

Stirling convertors↗

NASA NTRS2018

Automated Impact Assessment: A New Approach to ISS Payload Operations Anomaly Response

The International Space Station (ISS) Payload Operations and Integration Center (POIC) is undergoing rapid growth as the space station program focuses on science and commercial activities. The ISS is expanding its onboard capabilities to support additional science activities. In parallel, the POIC is expanding the capabilities of our operations tools to support the higher pace of payload activities being executed each week. An effect of these changes is that anomaly resolution has become more challenging. In the event of a real-time system fault, operators are responsible for analyzing telemetry displays, anomaly monitoring tools, documentation, and system models in order to produce failure impacts and recovery strategies. This approach to operations relies on the operator to ingest, process, and analyze information from an array of deterministic sources to provide actionable data on impacted systems and activities. Changing the existing approach of anomaly response is necessary if the ISS community is to succeed in the age of science and commercialization of space. The creation of a tool that captures deterministic technical systems knowledge and integrates existing telemetry, documentation, and planning information will allow the burden of impact assessment to be automated, thereby allowing the operator to focus on non-deterministic tasks, such as recovering failed systems and restoring critical payload operations.

Hall, R. Mason↗

NASA NTRS2014

Automated Discovery of Flight Track Anomalies

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high- volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.

Matthews, Bryan↗

NASA NTRS2020

Influence of Sea-Ice Anomalies on Antarctic Precipitation Using Source Attribution in the Community Earth System Model

We conduct sensitivity experiments using a general circulation model that has an explicit water source tagging capability forced by prescribed composites of pre-industrial sea-ice concentrations (SICs) and corresponding sea surface temperatures (SSTs) to understand the impact of sea-ice anomalies on regional evaporation, moisture transport and source–receptor relationships for Antarctic precipitation in the absence of anthropogenic forcing. Surface sensible heat fluxes, evaporation and column-integrated water vapor are larger over Southern Ocean (SO) areas with lower SICs. Changes in Antarctic precipitation and its source attribution with SICs have a strong spatial variability. Among the tagged source regions, the Southern Ocean (south of 50∘ S) contributes the most (40 %) to the Antarctic total precipitation, followed by more northerly ocean basins, most notably the South Pacific Ocean (27%), southern Indian Ocean (16 %) and South Atlantic Ocean (11 %). Comparing two experiments prescribed with high and low pre-industrial SICs, respectively, the annual mean Antarctic precipitation is about 150 Gt yr−1 (or 6 %) more in the lower SIC case than in the higher SIC case. This difference is larger than the model-simulated interannual variability in Antarctic precipitation (99 Gt yr−1). The contrast in contribution from the Southern Ocean, 102 Gt yr−1, is even more significant compared to the interannual variability of 35 Gt yr−1 in Antarctic precipitation that originates from the Southern Ocean. The horizontal transport pathways from individual vapor source regions to Antarctica are largely determined by large-scale atmospheric circulation patterns. Vapor from lower-latitude source regions takes elevated pathways to Antarctica. In contrast, vapor from the Southern Ocean moves southward within the lower troposphere to the Antarctic continent along moist isentropes that are largely shaped by local ambient conditions and coastal topography. This study also highlights the importance of atmospheric dynamics in affecting the thermodynamic impact of sea-ice anomalies associated with natural variability on Antarctic precipitation. Our analyses of the seasonal contrast in changes of basin-scale evaporation, moisture flux and precipitation suggest that the impact of SIC anomalies on regional Antarctic precipitation depends on dynamic changes that arise from SIC–SST perturbations along with internal variability. The latter appears to have a more significant effect on the moisture transport in austral winter than in summer.

Wang, Hailong↗

NASA NTRS2020

In-Flight Detection of Vibration Anomalies in Unmanned Aerial Vehicles

Owing to the frequency of occurrence and high risk associated with bearings, identification and characterization of bearing faults in motors via nondestructive evaluation (NDE) methods have been studied extensively, amongst which vibration analysis has been found to be a promising technique for early diagnosis of anomalies. However, a majority of the existing techniques rely on vibration sensors attached onto or in close proximity to the motor in order to collect signals with a relatively high SNR. Due to weight and space restrictions, these techniques cannot be used in unmanned aerial vehicles (UAVs), especially during flight operations since accelerometers cannot be attached onto motors in small UAVs. Small UAVs are often subjected to vibrational disturbances caused by multiple factors such as weather turbulence, propeller imbalance or bearing faults. Such anomalies may not only pose risks to UAV's internal circuitry, components or payload, they may also generate undesirable noise level particularly for UAVs expected to fly in low-altitudes or urban canyon. This paper presents a detailed discussion of challenges in in-flight detection of bearing failure in UAVs using existing approaches and offers potential solutions to detect overall vibration anomalies in small UAV operations based on IMU data.

Portia Banerjee↗

NASA NTRS2017

Telemetry Anomaly Detection System using Machine Learning to Streamline Mission Operations

Spacecraft housekeeping telemetry is monitored at flight control centers by the operations engineers using tools that can perform limit checking or simple trend analysis. Recent developments in machine learning techniques for anomaly detection enables the implementation of more sophisticated systems that aim to augment current state-of-theart mission tools to provide valuable decision support for the spacecraft operators, assisting in anomaly detection and potentially saving console time for the engineers. We will show some results of the implementation of an anomaly detection tool for the NASA Mars Science Laboratory mission.

Weber, Romann↗

NASA NTRSDate not supplied

Electromagnetic Thermocouple (TC) Anomaly Mechanisms During Atmospheric Entry

Thermocouples (TCs) are a critical diagnostic sensor for high enthalpy environments including Thermal Protection Systems (TPS) for atmospheric entry, hypersonic wind tunnels, and arc jet test facilities. They are used for both evaluating facility environmental conditions (enthalpy, and heat flux) as well as for measuring in-situ material thermal response under test conditions (thermal conductivity, heat capacity, etc.). Consequently, accurate TC measurements are fundamental for both material and facility Verification and Validation (V&V). However, a number of electromagnetic TC anomalies have been observed in both ground tests and flight instrumentations. These anomalies are caused via various coupling mechanisms: surface voltage coupling, Hall effect currents, and RF rectification. All of these mechanisms can, if unaddressed, result in obviously compromised results or worse, non-physical measurements which appear reasonable. This talk will provide an overview of each of these anomaly mechanisms and discuss ongoing efforts to quantify and mitigate them.

Magnus Haw↗

NASA NTRS2021

InSight Robotic Arm Testing Activities for HP3 Mole Anomaly Recovery on Mars

The InSight lander’s Heat Flow and Physical Properties Package (HP3 ) was deployed on Mars in February 2019 and began attempting to penetrate to its target depth range of 3-5 meters shortly thereafter. However, the mole’s downward progress stopped after only 35 cm of penetration. In response, the project convened an Anomaly Response Team (ART) and since then has been attempting to diagnose the problem and assist the mole using the tools available on Mars. The key asset used in this effort has been the Instrument Deployment System (IDS), which includes two cameras and a robotic Instrument Deployment Arm (IDA). Since the IDS was originally intended only to deploy InSight’s primary instruments to the Martian surface, new testbed setups, experiments, and operational protocols (e.g., command sequences) were required and had to be developed on a short timeline. The HP3 Mole ART first focused on gathering all observable data on Mars about the state of the mole and Support Structure Assembly (SSA), as well as the physical properties of the Martian regolith. This included using the robotic arm to point the IDC (Instrument Deployment Camera) at the SSA during diagnostic hammering attempts to observe motion of the SSA and science tether. Images taken during these attempts revealed some motion of the SSA, but no apparent change in mole depth. At JPL, the IDS and Testbed teams re-created the hardware configuration on Mars based on limited knowledge of the mole’s state. They devised and tested techniques to use the robotic arm and cameras to accomplish previously untested activities on Mars, including imaging the HP3 , using the IDA to interact with the terrain, and using the IDA to move the SSA away from the partially-embedded mole. The team executed the more promising techniques on Mars. After diagnostic hammering on Mars, the team decided to move the SSA to gain visibility of the mole’s configuration and access to the soil around the mole. After developing the technique and practicing the maneuver in the InSight testbed, the team lifted the SSA on Mars and placed it behind the mole. This revealed a pit surrounding the now exposed mole, observations of which provided essential clues for determining the root cause of the mole’s lack of progress. The IDS and Testbed teams altered the testbed to match the situation on Mars. They devised IDA techniques to determine the Martian soil properties and assist the mole’s descent. They tested these techniques in the testbed and executed the more promising ones on Mars. These include using the robotic arm to alter the regolith near the mole and to push on the mole while it hammers. This paper discusses the anomaly resolution testing in the testbed at JPL, describes how the IDS team prepared for the anomaly recovery activities on Mars, and provides preliminary results of the efforts to assist the HP3 mole on Mars.

Kim, Junggon↗

NASA NTRS2022

Systematic Kmtnet Planetary Anomaly Search V. Complete Sample of 2018 Prime-Field

We complete the analysis of all 2018 prime-field microlensing planets identified by the Korea Microlensing Telescope Network (KMTNet) Anomaly Finder. Among the ten previously unpublished events with clear planetary solutions, eight are either unambiguously planetary or are very likely to be planetary in nature: OGLE-2018-BLG-1126, KMT-2018-BLG-2004, OGLE-2018-BLG-1647, OGLE-2018-BLG-1367, OGLE-2018-BLG-1544, OGLE-2018-BLG-0932, OGLE-2018-BLG-1212, and KMT-2018-BLG-2718. Combined with the four previously published new Anomaly Finder events and 12 previously published (or in preparation) planets that were discovered by eye, this makes a total of 24 2018 prime-field planets discovered or recovered by Anomaly Finder. Together with a paper in preparation on 2018 subprime planets, this work lays the basis for the first statistical analysis of the planet mass-ratio function based on planets identified in KMTNet data. By systematically applying the heuristic analysis to each event, we identified the small modification in their formalism that is needed to unify the so-called close-wide and inner-outer degeneracies.

gravitational lensing: micro↗

NASA NTRSDate not supplied

Non-Additivity of the Midlatitude Circulation Response to Regional Arctic Temperature Anomalies: The Role of the Stratosphere

Previous studies have documented the impact of the Arctic sea ice loss and associated warming on the midlatitude weather and climate, especially the influence of sea ice retreat over the Barents-Kara Sea on the North Atlantic and Europe regions. However, less attention has been given to other geographical locations over the Arctic, and to the linear additivity of the circulation response to regional Arctic sea ice loss and temperature anomalies. Using a simplified dry dynamical core model, we demonstrate that responses to regional Arctic temperature anomalies over the Barents-Kara Sea, Baffin Bay-Davis Strait-Labrador Sea, and East Siberia-Chukchi Sea, separately, cause similar equatorward shift of the tropospheric jet, but different stratospheric polar vortex responses. Furthermore, responses to regional Arctic temperature anomalies are not linearly additive, and the residual resembles a positive Northern Annular Mode-like structure. Additional targeted experiments highlight the stratospheric influence in the non-additivity of the midlatitude tropospheric response.

Bithi De↗

DOE OSTI2020

Anomaly Detection in Sequential Health Care Data using Higher-Order Network Representation

The ever-increasing use of information technology (IT) in health care presents new challenges to patient care. Information errors arising from the use of Health IT and their implications on care delivery and patient outcomes have been widely reported. Information errors can lead to changes in clinical decisions, care processes, and care outcomes, among others. An altered care process, for example, could interrupt the sequence of care, which could lead to changes in care decisions and/or changes in care outcomes. We define interruptions in the care process as anomalies in the care sequence. In this paper, we present a new approach based on the higher-order network (HON) representation to detect anomalies in sequential health care data using electronic health records. The results show that there are higher-order dependencies in health care data; and that the use of HON representation is more effective than the first-order network representation for detecting anomalies in sequential health care data.

Niu, Haoran↗

DOE OSTI2022

Testing Explanations of Short Baseline Neutrino Anomalies

The experimental observation of neutrino oscillations profoundly impacted the physics of neutrinos, from being well understood theoretically to requiring new physics beyond the standard model of particle physics. Indeed, the mystery of neutrino masses implies the presence of new particles never observed before, often called sterile neutrinos, as they would not undergo standard weak interactions. And while neutrino oscillation measurements entered the precision era, reaching percent-level precision, many experimental results show significant discrepancies with the standard model, at baselines much shorter than typical oscillation baselines, like LSND, MiniBooNE, gallium experiments, and reactor antineutrino measurements. These short baseline anomalies could be explained by the addition of a light sterile neutrino, with mass in the $1-10~\text{eV}$ range, however, in strong tension with many null experimental observations. Other explanations that rely on sterile neutrinos with masses in the $1-500~\text{MeV}$ could resolve the tension. Here we test both classes of models. On the one hand, we look for datasets collected at a short baseline which can constrain heavy sterile neutrino models. We find that the minimal model is fully constrained, but several extensions of this model could weaken the current constraint and be tested with current and future datasets. On the other hand, we test the presence of neutrino oscillations at short baselines, induced by a light sterile state, with the data collected by the MicroBooNE experiment, a liquid argon time projection chamber specifically designed to resolve the details of each neutrino interaction. We report null results from both analyses, further constraining the space of possible explanations for the short baseline anomalies. If new physics lies behind the short baseline anomaly puzzle, it is definitely not described by a simple model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Systems and methods for powder bed additive manufacturing anomaly detection

Detection and classification of anomalies for powder bed metal additive manufacturing. Anomalies, such as recoater blade impacts, binder deposition issues, spatter generation, and some porosities, are surface-visible at each layer of the building process. A multi-scaled parallel dynamic segmentation convolutional neural network architecture provides additive manufacturing machine and imaging system agnostic pixel-wise semantic segmentation of layer-wise powder bed image data. Learned knowledge is easily transferrable between different additive manufacturing machines. The anomaly detection can be conducted in real-time and provides accurate and generalizable results.

Scime, Luke R.↗

DOE OSTI2024

Rapid Detection of Anomalies in Battery Energy Storage System Data

Data analytics is pivotal in assessing the technical characteristics and performance of Battery Energy Storage Systems (BESS), underpinning BESS modeling, optimization, and control. However, raw datasets frequently harbor anomalies from measurement errors and equipment malfunctions, impacting BESS reliability and analysis accuracy To address the challenge, this paper presents a novel methodology for the rapid detection of anomalous charge or discharge cycles within BESS operational data, expediting the cleaning process while ensuring data integrity. We’ve collected diverse and comprehensive real-world BESS operational datasets in collaboration with the Electric Power Research Institute and multiple Washington State utilities. These datasets serve dual roles: enabling comprehensive data exploration and analysis for understanding underlying challenges and method development, while also acting as a vital validation resource, demonstrating practical effectiveness. The proposed method detects anomalies and aids in their resolution, improving system performance characterization precision. It also reveals recurring data anomaly sources, offering insights for data collection and handling enhancement. Practitioners can gain valuable insights from the identified anomalous cycles in the real-world datasets along with the investigative process for root cause analyses and essential data cleaning steps.

Crawford, Aladsair J.↗

DOE OSTI2025

Anomaly Detection in DUNE FD LArTPC Readouts

We designed and built an AI/ML model to detect anomalies in the DUNE far detector data. The model has been trained on simulated radiological background (rbkg) data, which is the major background for supernova burst neutrinos that we want to detect as anomaly in thios work. The trained model was evaluated on both new samples of radiological backgrounds and supernova burst neutrino events in the elastic scattering and charged current interaction channels. We found that the trained model can successfully identify supernova burst neutrino events as anomalies while identifying radiological backgrounds as nominal events.

Novello, Eric [Fermilab]↗