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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.

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30 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Extraction of Qualitative Features from Sensor Data Using Windowed Fourier Transform

In this paper, we use Matlab to model the health monitoring of a system through the information gathered from sensors. This implies assessment of the condition of the system components. Once a normal mode of operation is established any deviation from the normal behavior indicates a change. This change may be due to a malfunction of an element, a qualitative change, or a change due to a problem with another element in the network. For example, if one sensor indicates that the temperature in the tank has experienced a step change then a pressure sensor associated with the process in the tank should also experience a step change. The step up and step down as well as sensor disturbances are assumed to be exponential. An RC network is used to model the main process, which is step-up (charging), drift, and step-down (discharging). The sensor disturbances and spike are added while the system is in drift. The system is allowed to run for a period equal to three time constant of the main process before changes occur. Then each point of the signal is selected with a trailing data collected previously. Two trailing lengths of data are selected, one equal to two time constants of the main process and the other equal to two time constants of the sensor disturbance. Next, the DC is removed from each set of data and then the data are passed through a window followed by calculation of spectra for each set. In order to extract features the signal power, peak, and spectrum are plotted vs time. The results indicate distinct shapes corresponding to each process. The study is also carried out for a number of Gaussian distributed noisy cases.

Amini, Abolfazl M.↗
NASA NTRS2004

Qualitative Features Extraction from Sensor Data using Short-time Fourier Transform

The information gathered from sensors is used to determine the health of a sensor. Once a normal mode of operation is established any deviation from the normal behavior indicates a change. This change may be due to a malfunction of the sensor(s) or the system (or process). The step-up and step-down features, as well as sensor disturbances are assumed to be exponential. An RC network is used to model the main process, which is defined by a step-up (charging), drift, and step-down (discharging). The sensor disturbances and spike are added while the system is in drift. The system runs for a period of at least three time-constants of the main process every time a process feature occurs (e.g. step change). The Short-Time Fourier Transform of the Signal is taken using the Hamming window. Three window widths are used. The DC value is removed from the windowed data prior to taking the FFT. The resulting three dimensional spectral plots provide good time frequency resolution. The results indicate distinct shapes corresponding to each process.

Amini, Abolfazl M.↗
NASA NTRS1999

Problems Associated with Statistical Pattern Recognition of Acoustic Emission Signals in a Compact Tension Fatigue Specimen

Acoustic emission (AE) data were acquired during fatigue testing of an aluminum 2024-T4 compact tension specimen using a commercially available AE system. AE signals from crack extension were identified and separated from noise spikes, signals that reflected from the specimen edges, and signals that saturated the instrumentation. A commercially available software package was used to train a statistical pattern recognition system to classify the signals. The software trained a network to recognize signals with a 91-percent accuracy when compared with the researcher's interpretation of the data. Reasons for the discrepancies are examined and it is postulated that additional preprocessing of the AE data to focus on the extensional wave mode and eliminate other effects before training the pattern recognition system will result in increased accuracy.

Hinton, Yolanda L.↗
NASA NTRS2021

Oklahoma Health and Air Quality: Mapping Air Quality Using NASA Earth Observations to Investigate Recent Increases in Ozone Concentrations

Tropospheric ozone (O3) is formed by anthropogenic pollutants interacting with sunlight and is considered harmful to human health in high concentrations. In the summer of 2018, the Oklahoma Department of Environmental Quality (DEQ) measured unexpected spikes in O3 in Seiling, Oklahoma, with concentrations exceeding those measured in bustling Oklahoma City and Tulsa. The DEQ tracks air quality using ground monitors and does not utilize Earth observation data in its monitoring or analysis. This project used remotely sensed data to investigate these2018 air quality anomalies, identifying possible causes. We analyzed atmospheric data from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and Sentinel-5P Tropospheric Ozone Monitoring Instrument (TROPOMI) in conjunction with ground-based measurements of tropospheric ozone (O3), nitrogen dioxide (NO2), methane (CH4), carbon monoxide (CO), formaldehyde (HCHO) and aerosol optical depth (AOD). We compared Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model simulations and Earth observation visualizations to pinpoint ozone spike causes. We also generated models to identify contributing factors to variations in ground ozone concentrations in our study area. The results point to a variety of ozone spike causes, primarily from outside of the state, and support the placement of additionalNO2, O3, and CO monitors to the southeast of Seiling. These analyses can help guide the placement of future monitors in the ground monitoring network and inform air quality regulations in Oklahoma.

Carolina Rosales↗
NASA NTRS2023

Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid-Latitude Stations

The prediction of large fluctuations in the ground magnetic field (dB/dt) is essential for preventing damage from Geomagnetically Induced Currents. Directly forecasting these fluctuations has proven difficult, but accurately determining the risk of extreme events can allow for the worst of the damage to be prevented. Here we trained Convolutional Neural Network models for eight mid-latitude magnetometers to predict the probability that dB/dt will exceed the 99th percentile threshold 30–60 min in the future. Two model frameworks were compared, a model trained using solar wind data from the Advanced Composition Explorer (ACE) satellite, and another model trained on both ACE and SuperMAG ground magnetometer data. The models were compared to examine if the addition of current ground magnetometer data significantly improved the forecasts of dB/dt in the future prediction window. A bootstrapping method was employed using a random split of the training and validation data to provide a measure of uncertainty in model predictions. The models were evaluated on the ground truth data during eight geomagnetic storms and a suite of evaluation metrics are presented. The models were also compared to a persistence model to ensure that the model using both datasets did not over-rely on dB/dt values in making its predictions. Overall, we find that the models using both the solar wind and ground magnetometer data had better metric scores than the solar wind only and persistence models, and was able to capture more spatially localized variations in the dB/dt threshold crossings.

Michael Coughlan↗
NASA NTRS1991

Long range science scheduling for the Hubble Space Telescope

Observations with NASA's Hubble Space Telescope (HST) are scheduled with the assistance of a long-range scheduling system (SPIKE) that was developed using artificial intelligence techniques. In earlier papers, the system architecture and the constraint representation and propagation mechanisms were described. The development of high-level automated scheduling tools, including tools based on constraint satisfaction techniques and neural networks is described. The performance of these tools in scheduling HST observations is discussed.

Miller, Glenn↗
NASA NTRS1980

The unique cosmic event of 1979 March 5

The transient gamma-ray burst observed on March 5, 1979 by the nine spacecraft forming the interplanetary gamma-ray burst network is discussed. Measurements reveal the event to be unlike any previously observed gamma-ray burst or X-ray burster, with a maximum intensity greater than several thousandths erg/sq cm per sec, a rise time of less than 200 microsec, a narrow and featureless initial spike and a regular 8-sec oscillation with a compound pulse shape. The source of the transient has been localized to a region including the supernova remnant N 49 in the Large Magellanic Cloud, at a distance of 55 pc, and a ratio of X-ray point-source steady state to transient emission of less than 10 to the -9th was obtained. The identification of the burst source with the supernova remnant at that distance would require an enormous source output if it was radiated isotropically, and substantially less if beaming is present. Alternatively, the event could be explained by a nearby, invisible neutron star.

Cline, T. L.↗
NASA NTRS1989

Spike train generation and current-to-frequency conversion in silicon diodes

A device physics model is developed to analyze spontaneous neuron-like spike train generation in current driven silicon p(+)-n-n(+) devices in cryogenic environments. The model is shown to explain the very high dynamic range (0 to the 7th) current-to-frequency conversion and experimental features of the spike train frequency as a function of input current. The devices are interesting components for implementation of parallel asynchronous processing adjacent to cryogenically cooled focal planes because of their extremely low current and power requirements, their electronic simplicity, and their pulse coding capability, and could be used to form the hardware basis for neural networks which employ biologically plausible means of information coding.

Coon, D. D.↗
NASA NTRS2016

Observations of Two Sprite-Producing Storms in Colorado

Two sprite-producing thunderstorms were observed on 8 and 25 June 2012 in northeastern Colorado by a combination of low-light cameras, a lightning mapping array, polarimetric and Doppler radars, the National Lightning Detection Network, and charge moment change measurements. The 8 June event evolved from a tornadic hailstorm to a larger multicellular system that produced 21 observed positive sprites in 2 h. The majority of sprites occurred during a lull in convective strength, as measured by total flash rate, flash energy, and radar echo volume. Mean flash area spiked multiple times during this period; however, total flash rates still exceeded 60 min(sup 1), and portions of the storm featured a complex anomalous charge structure, with midlevel positive charge near 20degC. The storm produced predominantly positive cloud-to-ground lightning. All sprite-parent flashes occurred on the northeastern flank of the storm, where strong westerly upper level flow was consistent with advection of charged precipitation away from convection, providing a pathway for stratiform lightning. The 25 June event was another multicellular hailstorm with an anomalous charge structure that produced 26 positive sprites in less than 1 h. The sprites again occurred during a convective lull, with relatively weaker reflectivity and lower total flash rate but relatively larger mean flash area. However, all sprite parents occurred in or near convection and tapped charge layers in adjacent anvil cloud. The results demonstrate the sprite production by convective ground strokes in anomalously charged storms and also indicate that sprite production and convective vigor are inversely related in mature storms.

Radar↗
NASA NTRS2001

A model of cerebellar computations for dynamical state estimation

The cerebellum is a neural structure that is essential for agility in vertebrate movements. Its contribution to motor control appears to be due to a fundamental role in dynamical state estimation, which also underlies its role in various non-motor tasks. Single spikes in vestibular sensory neurons carry information about head state. We show how computations for optimal dynamical state estimation may be accomplished when signals are encoded in spikes. This provides a novel way to design dynamical state estimators, and a novel way to interpret the structure and function of the cerebellum.

NASA Discipline Neuroscience↗
NASA NTRS2002

Fatigue Crack Measurement in Composite Materials by Ultrasonic Methods

The nondestructive detection of intra-ply microcracking in unlined pressure vessels fabricated from composite materials is critical to ensuring mission success. Microcracking in composite structures due to combined fatigue and cryogenic thermal loading can be very troublesome to detect in-service and when it begins to link through the thickness can cause leakage and failure of the structure. These leaks may lead to loss of pressure/propellant, increased risk of explosion and possible cryo-pumping. The work presented herein develops a method and an instrument to locate and measure intraply fatigue cracking through the thickness of laminated composite material by means of correlation with ultrasonic resonance. Resonant ultrasound spectroscopy provides measurements which are, sensitive to both the microscopic and macroscopic properties of an object. Elastic moduli, acoustic attenuation, and geometry can all be probed. The approach is based on the premise of half-wavelength resonance. The method injects a broadband ultrasonic wave into the test structure using a swept frequency technique. This method provides dramatically increased energy input into the test article, as compared to conventional spike pulsed ultrasonics. This relative energy increase improves the ability to measure finer details in the materials character, such as micro-cracking and porosity. As the micro-crack density increases, more interactions occur with the higher frequency (small wavelength) components of the signal train causing the spectrum to shift toward lower frequencies. Preliminary experiments have verified a measurable effect on the resonance spectrum of the ultrasonic data to detect microcracking. Methods involving self organizing neural networks and other clustering algorithms show that the resonance ultrasound signatures from composites vary with the degree of microcracking and can be separated and identified.

Walker, James L.↗
NASA NTRS1990

An approach to rescheduling activities based on determination of priority and disruptivity

A constraint-based scheduling system called SPIKE is being used to create long term schedules for the Hubble Space Telescope. Feedback for the spacecraft or from other ground support systems may invalidate some scheduling decisions and those activities concerned must be reconsidered. A function rescheduling priority is defined which for a given activity performs a heuristic analysis and produces a relative numerical value which is used to rank all such entities in the order that they should be rescheduled. A function disruptivity is also defined that is used to place a relative numeric value on how much a pre-existing schedule would be changed in order to reschedule an activity. Using these functions, two algorithms (a stochastic neural network approach and an exhaustive search approach) are proposed to find the best place to reschedule an activity. Prototypes were implemented and preliminary testing reveals that the exhaustive technique produces only marginally better results at much greater computational cost.

Sponsler, Jeffrey L.↗