SEARCH · Engineering Papers

Results for “Anomaly”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5

All sources NASA NTRS DOE OSTI

NASA NTRS2011

Does Terrestrial Drought Explain Global CO2 Flux Anomalies Induced by El Nino?

The El Nino Southern Oscillation is the dominant year-to-year mode of global climate variability. El Nino effects on terrestrial carbon cycling are mediated by associated climate anomalies, primarily drought, influencing fire emissions and biotic net ecosystem exchange (NEE). Here we evaluate whether El Nino produces a consistent response from the global carbon cycle. We apply a novel bottom-up approach to estimating global NEE anomalies based on FLUXNET data using land cover maps and weather reanalysis. We analyze 13 years (1997-2009) of globally gridded observational NEE anomalies derived from eddy covariance flux data, remotely-sensed fire emissions at the monthly time step, and NEE estimated from an atmospheric transport inversion. We evaluate the overall consistency of biospheric response to El Nino and, more generally, the link between global CO2 flux anomalies and El Nino-induced drought. Our findings, which are robust relative to uncertainty in both methods and time-lags in response, indicate that each event has a different spatial signature with only limited spatial coherence in Amazonia, Australia and southern Africa. For most regions, the sign of response changed across El Nino events. Biotic NEE anomalies, across 5 El Nino events, ranged from -1.34 to +0.98 Pg Cyr(exp -1, whereas fire emissions anomalies were generally smaller in magnitude (ranging from -0.49 to +0.53 Pg C yr(exp -1). Overall drought does not appear to impose consistent terrestrial CO2 flux anomalies during El Ninos, finding large variation in globally integrated responses from 11.15 to +0.49 Pg Cyr(exp -1). Despite the significant correlation between the CO2 flux and El Nino indices, we find that El Nino events have, when globally integrated, both enhanced and weakened terrestrial sink strength, with no consistent response across events

Schwalm. C. R.↗

NASA NTRS2018

Determination of Total Magnetic Anomalies and Their Vertical Gradients of Swarm-A Satellite over Central Europe and Pannonian Basin

Our paper discusses the determination of total magnetic field anomalies derived from the Swarm–A satellite data; one of two low orbiting satellites of the three Swarm formations. This procedure requires several modifications. The first step is the conversion of the measured CDF data to the ASCII format. This step is followed by the selection of the data with K(sub p) index ≤ 1(sub +). The anomalies are determined by the removal of the IGRF from the resulting satellite data. There are two Swarm–A data sets: descending (6000) orbits and ascending (5688) orbits. For our study the descending orbits were used. The next step of calculations is to determine the difference of the two-dimensional linear field fitted to the Swarm–A anomalies and the anomalies given. These anomalies are filtered by Gaussian low-pass filter. The last step of the corrections is the removal of the direct component, zero spatial frequency, from the descending anomalies. The anomalies and their vertical gradients are qualitatively interpreted over Central Europe and the Pannonian Basin.

Kis, K.↗

NASA NTRS2019

GRAIL-Identified Gravity Anomalies in Oceanus Procellarum: Insight into Subsurface Impact and Magmatic Structures on the Moon

Four, quasi-circular, positive Bouguer gravity anomalies (PBGAs) that are similar in diameter (~90-190 km) and gravitational amplitude (> 140 mGal contrast) are identified within the central Oceanus Procellarum region of the Moon. These spatially associated PBGAs are located south of Aristarchus Plateau, north of Flamsteed crater, and two are within the Marius Hills volcanic complex (north and south). Each is characterized by distinct surface geologic features suggestive of ancient impact craters and/or volcanic/plutonic activity. Here, we combine geologic analyses with forward modeling of high-resolution gravity data from the Gravity Recovery and Interior Laboratory (GRAIL) mission in order to constrain the subsurface structures that contribute to these four PBGAs. The GRAIL data presented here, at spherical harmonic degrees 6–660, permit higher resolution analyses of these anomalies than previously reported, and reveal new information about subsurface structures. Specifically, we find that the amplitudes of the four PBGAs cannot be explained solely by mare-flooded craters, as suggested in previous work; an additional density contrast is required to explain the high-amplitude of the PBGAs. For Northern Flamsteed (190 km diameter), the additional density contrast may be provided by impact-related mantle uplift. If the local crust has a density ~2800 kg/cu.m, then ~7 km of uplift is required for this anomaly, although less uplift is required if the local crust has a lower mean density of ~2500 kg/cu.m. For the Northern and Southern Marius Hills anomalies, the additional density contrast is consistent with the presence of a crustal complex of vertical dikes that occupies up to ~50% of the regionally thin crust. The structure of Southern Aristarchus Plateau (90 km diameter), an anomaly with crater-related topographic structures, remains ambiguous. Based on the relatively small size of the anomaly, we do not favor mantle uplift; however, understanding mantle response in a region of especially thin crust needs to be better resolved. It is more likely that this anomaly is due to subsurface magmatic material given the abundance of volcanic material in the surrounding region. Overall, the four PBGAs analyzed here are important in understanding the impact and volcanic/plutonic history of the Moon, specifically in a region of thin crust and elevated temperatures characteristic of the Procellarum KREEP Terrane.

Deutsch, Ariel N.↗

NASA NTRS2020

MSL Telecom Automated Anomaly Detection

The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.

Mukai, Ryan↗

NASA NTRSDate not supplied

Air Data Probe Anomalies in Flight through Measured High Ice Water Content Conditions

High concentrations of ice crystals in convective storms have caused anomalous air temperature and airspeed readings during commercial and research flight operations. These anomalies occur when ice crystals are ingested in the heated probe inlet, melt or partially melt to liquid water, and then refreeze or remain in a liquid state depending on the probe heat and cloud conditions. In pitot probes, the refreezing may cause complete blockage of the total pressure, which causes airspeed anomalies. In total air temperature probes, the melted ice water may flow near the temperature sensing element and cause the total air temperature reading to approach 0 degree Celsius. During the High Ice Water Content (HIWC) RADAR and HIWC-2022 flight campaigns and the Convective Process Experiment (CPEX-CV) flight campaign, a total of 71 anomalies were recorded on the NASA DC-8 pitot probes when subjected to specific flight and cloud conditions. Similarly, a research TAT probe mounted near the pitot probes had 19 anomalies. This paper presents analyses of the measured natural conditions that led to these TAT and pitot anomalies, identifies two types of pitot probe anomalies, applies a concentration factor to estimate local TWC conditions near the TAT and pitot probes, and identifies the static air temperature and pressure altitude where the anomalies occurred on the Part 33 Appendix D envelope.

Aircraft Icing↗

NASA NTRSDate not supplied

Air Data Probe Anomalies in Flight through Measured High Ice Water Content Conditions

Aircraft Icing↗

DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗

DOE OSTI2025

Monte Carlo Dropout Uncertainty Quantification of Long Short-Term Memory Autoencoder Anomaly Detection in a Liquid Sodium Cold Trap

Advanced high-temperature fluid reactors, such as sodium-cooled fast reactors (SFRs) and molten salt–cooled reactors (MSCRs), require coolant purification systems to prevent fluid contamination and local freezing that can lead to plugging. Liquid sodium purification can be achieved with a cold trap, where the sodium temperature is reduced to a near-freezing point to precipitate out impurities. Automation of monitoring of the cold trap performance with machine learning algorithms can aid in early detection of incipient anomalies. An efficient approach to loss-of-coolant–type anomaly detection in a cold trap monitored with more than two dozen thermal-hydraulic sensors consists of a long short-term memory (LSTM) autoencoder. This work develops the uncertainty quantification of the LSTM autoencoder performance for cold trap anomaly detection using the Monte Carlo (MC) dropout method. The MC dropout methodology creates a distribution of sister distributions that all slightly differ from each other because of random neurons being turned off for testing. The variances of the sister network distributions are used to make an uncertainty interval. Our analysis shows that the uncertainty in the autoencoder performance is largest near the peak of the anomaly signal. Using the MC dropout method, we investigate the uncertainty in the anomaly detection with missing sensor inputs. This capability allows the reactor operator to evaluate resilience of the anomaly detection system and to make informed decisions about continuity of operation in the event of sensor failure.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the Spallation Neutron Source

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory accelerates proton beams, which are directed toward a mercury target to generate the world’s most intense neutron beams via spallation. The target system consists of several interconnected subsystems and accounts for a major share of the facility’s overall downtime. Early detection of anomalies in the target system response can thus provide the possibility of taking corrective actions to reduce downtime. Accelerator facilities have largely focused on the beam side for data-driven fault prognostics. On the target side, SNS relies on operational shift technicians (OSTs), who respond to alarms and manually flag anomalies onto the System Tracking and Reliability (STAR) platform. This paper presents one of the first studies of using machine learning (ML) to automate anomaly detection in the target system. The study focused on the mercury process system as the first use case and employed reconstruction-based anomaly detection on minutely sampled time series signals. The pipeline was integrated into the STAR platform to autonomously rank and flag anomalies every week. The STAR platform provides a user interface for the OSTs to evaluate the flagged anomalies, thereby incorporating human feedback.

Anomaly detection↗

NASA NTRSDate not supplied

Tensor Decomposition Analysis for UAV Anomaly Detection

Vibrational anomalies can provide valuable insights into the health status of an unmanned aerial vehicle, potentially indicating system degradation including propeller, motor, or sensor damage, as well as environmental anomalies such as strong wind gusts and turbulence. However, many causes for vibrational anomalies are not related to vehicle health, such as sharp shifts in velocity or direction of flight. Thus, depending strictly on vibration signals to detect anomalies can result in false positives for failures. Hence, it is important to include additional telemetries in detecting and diagnosing in-flight anomalies. This paper considers an approach to anomaly detection based on tensor decompositions that incorporates information from vibration signals, as well as additional flight data such as velocity, current draw, voltage drop, and attitude. Using experimental flight data collected by the University of Notre Dame, we construct third-order tensors then apply the CANDECOMP/PARAFAC decomposition to identify trends within each flight and classify flights as nominal or anomalous.

unmanned aviation↗

DOE OSTI2024

Autoencoder-Based Anomaly Detection System for Online Data Quality Monitoring of the CMS Electromagnetic Calorimeter

The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

High-Fidelity Dataset Generation for Sensor Anomalies in Power Grids using Hardware-in-the-Loop Testbed

Sensor anomalies in power grids can have significant impacts on the operation of the grid due to the increased reliance of the grid operation on data-driven applications. However, there is a lack of datasets that accurately capture these anomalies as many of the anomalies go undetected using the current bad data detectors. High-fidelity labeled datasets are essential for developing robust applications that can detect and mitigate the impacts of anomalies. In this paper, we propose a hardware-in-the-loop testbed model that can emulate the grid behavior with high-fidelity. This testbed is used to inject anomalies at various levels in the grid architecture and generate labeled datasets. These high-fidelity datasets can be used for development and validation of data-driven applications for detection and mitigation of anomalies in grids and other cyber-physical systems.

Hyder, Burhan↗

DOE OSTI2025

Revisiting the Role of Ocean Circulation Changes in Polar Ocean Heat Transport Anomalies under Global Warming

In response to greenhouse gas forcing, climate models predict that poleward ocean heat transport (OHT) weakens in the Southern Ocean but increases in the Arctic. The role of ocean circulation changes in this OHT response has been evaluated by decomposing OHT anomalies into a dynamic component (holding ocean temperature fixed while circulation evolves) and a thermodynamic component (holding ocean circulation fixed while temperature evolves). However, ocean temperature changes are themselves shaped by circulation changes through redistribution of the existing heat reservoir and subsequent effects on air–sea heat fluxes. The thermodynamic component can therefore be influenced by circulation changes, making the standard thermodynamic–dynamic decomposition incomplete for isolating the role of circulation changes in OHT anomalies. To address this issue, we use a passive–active decomposition to assess the relative contributions of ocean circulation and passive ocean temperature changes to polar OHT anomalies in a fully coupled climate model. Passive temperature changes are defined as those thermally forced by the atmosphere in the absence of circulation changes. In this passive–active decomposition, an advective term involving both circulation and passive temperature changes remains ambiguous—classifying it as active implies circulation changes dominate Southern Ocean OHT anomalies, whereas classifying it as passive implies temperature changes dominate. However, both interpretations imply that ocean circulation changes have a much weaker effect on polar OHT anomalies than inferred from the standard decomposition. In conclusion, these results help reconcile conclusions from studies using the standard decomposition with those using passive tracer methods to assess the role of circulation changes in polar OHT anomalies.

Arctic↗

NASA NTRS1973

Supplementary notes on sea-surface temperature anomalies and model-generated meteorological histories

A numerical experiment was made on the Mintz-Arakawa two-level global general circulation model, investigating the effects of a transient one-month sea-surface temperature (SST) anomaly and of a persistent three-month SST anomaly. The time histories are compared with a control run that lacked the anomaly pattern. After one month, the anomaly and control synoptic patterns were as uncorrelated as any two random fields. It is found that the transient SST anomaly can alter the monthly and seasonal temperature and precipitation in the eastern U.S. by as much as two class intervals. The question of whether the real atmosphere is as sensitive to such an anomaly is raised but cannot be settled at this time.

Spar, J.↗

NASA NTRS1977

On the origin of the Bangui magnetic anomaly, central African empire

A large magnetic anomaly was recognized in satellite magnetometer data over the Central African Empire in central Africa. They named this anomaly the Bangui magnetic anomaly due to its location near the capital city of Bangui, C.A.E. Because large crustal magnetic anomalies are uncommon, the origin of this anomaly has provoked some interest. The area of the anomaly was visited to make ground magnetic measurements, geologic observations, and in-situ magnetic susceptibility measurements. Some rock samples were also collected and chemically analyzed. The results of these investigations are presented.

Marsh, B. D.↗

NASA NTRS1978

On isostatic geoid anomalies

In regions of slowly varying lateral density changes, the gravity and geoid anomalies may be expressed as power series expansions in topography. Geoid anomalies in isostatically compensated regions can be directly related to the local dipole moment of the density-depth distribution. This relationship is used to obtain theoretical geoid anomalies for different models of isostatic compensation. The classical Pratt and Airy models give geoid height-elevation relationships differing in functional form but predicting geoid anomalies of comparable magnitude. The thermal cooling model explaining ocean floor subsidence away from mid-ocean ridges predicts a linear age-geoid height relationship of 0.16 m/m.y. Geos 3 altimetry profiles were examined to test these theoretical relationships. A profile over the mid-Atlantic ridge is closely matched by the geoid curve derived from the thermal cooling model. The observed geoid anomaly over the Atlantic margin of North America can be explained by Airy compensation. The relation between geoid anomaly and bathymetry across the Bermuda Swell is consistent with Pratt compensation with a 100-km depth of compensation.

Haxby, W. F.↗

NASA NTRS1979

The moon - Sources of the crustal magnetic anomalies

Evidence from low altitude Apollo 16 subsatellite magnetometer measurements of the lunar near side is presented showing that basin and crater ejecta are major sources of lunar magnetic anomalies. It is found that anomalies increase in amplitude and complexity with a decrease in altitude, indicating localized, near surface sources, but it is also found that anomalies are less intense and numerous over maria than over highlands. Also, few anomalies are found to be associated with young craters, eliminating direct shock magnetization as a possible cause, but numerous anomalies are present over formations not flooded by mare basalts. The largest observed anomaly is well correlated with the location of a conspicuous deposit, believed to be crater ejecta, with a mean magnetization level of 5.2 + or - 2.4 x 10 to the -2 electromagnetic units/g. It is also concluded that the magnetization of the lunar crust must have taken place over a period of over one billion years.

Hood, L. L.↗

NASA NTRS1980

The elliptic anomaly

An independent variable different from the time for elliptic orbit integration is used. Such a time transformation provides an analytical step-size regulation along the orbit. An intermediate anomaly (an anomaly intermediate between the eccentric and the true anomaly) is suggested for optimum performances. A particular case of an intermediate anomaly (the elliptic anomaly) is defined, and its relation with the other anomalies is developed.

Janin, G.↗