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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Microbial load monitor

Device can detect and identify a number of medically important microorganisms in an average of approximately 8 hours. Monitor consists of cartridges containing special selective media and solid state electro-optical detectors.

Jones, P. W.↗
NASA NTRS1976

Microbial load monitor

A card configuration which combines the functions of identification, enumeration and antibiotic sensitivity into one card was developed. An instrument package was designed around the card to integrate the card filling, incubation reading, computation and decision making process into one compact unit. Support equipment was also designed to prepare the expandable material used in the MLM.

Holen, J. T.↗
NASA NTRS2014

Evaluation of the XSENS Force Shoe on ISS

The Advanced Resistive Exercise Device (ARED) offers crewmembers a wide range of resistance exercises but does not provide any type of load monitoring; any load data received are based on crew self-report of dialed in load. This lack of real-time ARED load monitoring severely limits research analysis. To address this issue, portable load monitoring technologies are being evaluated to act as a surrogate to ARED's failed instrumentation. The XSENS ForceShoe"TM" is a commercial portable load monitoring tool, and performed well in ground tests. The ForceShoe "TM" was recently deployed on the International Space Station (ISS), and is being evaluated as a tool to monitor ARED loads.

Hanson, A. M.↗
NASA NTRS2015

Ultrasonic Measurement and Monitoring of Loads in Bolts used in Structural Joints

The paper is an overview of work by the author in measuring and monitoring loads in bolts using an ultrasonic extensometer. A number of cases of bolted joints are covered. These include, a clamped joint with clearance fit between the bolt and hole, a clamped joint with the bolt in an interference fit with the hole, a flanged joint which allows the flange and bolt to bend; and a shear joint in a clevis and tang configuration. These applications were initially developed for measuring and monitoring preload in the NASA Space Shuttle Orbiter critical joints but are also applicable for monitoring loads in other critical bolted joints of structures such as transportation bridges and other aerospace structures. The paper explains how to set‐up a model to estimate the load factor and accuracy for the ultrasonic preload application in a clamped joint with clearance fit. The ultrasonic preload application for clamped joint with bolt in an interference fit can also be used to measure diametrical interference between the bolt shank and hole; and interference pressure on the bolt shank. Model and experimental data are given to demonstrate use of ultrasonic measurements in a shear joint. A bolt in a flanged joint experiences both tensile and bending loads. This application involves measurement of bending and tensile preload in a bolt. The ultrasonic beam bends due to the bending load on the bolt. A numerical technique to compute the trace of ultrasonic ray is presented.

Koshti, Ajay↗
NASA NTRS1989

Monitoring dynamic loads on wind tunnel force balances

Two devices have been developed at NASA Langley to monitor the dynamic loads incurred during wind-tunnel testing. The Balance Dynamic Display Unit (BDDU), displays and monitors the combined static and dynamic forces and moments in the orthogonal axes. The Balance Critical Point Analyzer scales and sums each normalized signal from the BDDU to obtain combined dynamic and static signals that represent the dynamic loads at predefined high-stress points. The display of each instrument is a multiplex of six analog signals in a way that each channel is displayed sequentially as one-sixth of the horizontal axis on a single oscilloscope trace. Thus this display format permits the operator to quickly and easily monitor the combined static and dynamic level of up to six channels at the same time.

Ferris, Alice T.↗
DOE OSTI2022

Intelligent Energy Optimizer for Residential Buildings

Demand-side management in the buildings is essential for meeting grid flexibility needs in a highly renewable energy scenario. Appliance load monitoring helps decision making for demand-side management by providing the information on operation status/power consumption from different appliances in the buildings. Nonintrusive load monitoring (NILM) is an attractive option for appliance load monitoring using because it has lower cost for sensors and helps mitigate privacy concerns. In this study, the team used an event detection technique followed by two different methods for event classification. The results from k-means clustering showed that the events from a single appliance are often distributed in multiple clusters. Thus, the unsupervised method of NILM using k-means clustering used in this study was not very suitable for load disaggregation. The results from NILM showed that the F1 score for event classification was 0.77 for a heat pump water heater and very low for other appliances using the rule-based classification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2001

Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction

Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.

NASA Discipline Space Human Factors↗
DOE OSTI2024

Improving Robustness of Spectrogram Classifiers with Neural Stochastic Differential Equations

Signal analysis and classification is fraught with high levels of noise and perturbation. Computer-vision-based deep learning models applied to spectrograms have proven useful in the field of signal classification and detection; however, these methods aren't designed to handle the low signal-to-noise ratios inherent within non-vision signal processing tasks. While they are powerful, they are currently not the method of choice in the inherently noisy and dynamic critical infrastructure domain, such as smart-grid sensing, anomaly detection, and non-intrusive load monitoring. Currently, these models can be brittle, which makes them susceptible to noisy input. This also means they have sub-optimal stability of explanation outputs. Experts and technicians using these models to make decisions in real world scenarios need assurance that a model is performing as it is supposed to. The classification or prediction outputs it generates should be sound and grounded, not likely to change in the presence of shifting noise landscapes. In this work, we explore the idea of Neural Stochastic Differential Equations (NSDE's) to improve the robustness of models trained to classify time series data and the effect of NSDE's on the explainability of outputs. We then test the effectiveness of these approaches by applying them to a non-intrusive load monitoring (NILM) dataset that consists of simulated harmonic signals injected into a real building.

Brogan, Joel↗
DOE OSTI2021

Preprocessing for Unintended Conducted Emissions Classification with ResNet

Characterization of Unintended Conducted Emissions (UCE) from electronic devices is important when diagnosing electromagnetic interference, performing nonintrusive load monitoring (NILM) of power systems, and monitoring electronic device health, among other applications. Prior work has demonstrated that UCE analysis can serve as a diagnostic tool for energy efficiency investigations and detailed load analysis. While explaining the feature selection of deep networks with certainty is often not fully comprehensive, or in other applications, quite lacking, additional tools/methods for further corroboration and confirmation can help further the understanding of the researcher. This is true especially in the subject application of the study in this paper. Often the focus of such efforts is the selected features themselves, and there is not as much understanding gained about the noise in the collected data. If selected feature and noise characteristics are known, it can be used to further shape the design of the deep network or associated preprocessing. This is additionally difficult when the available data are limited, as in the case which the authors investigated in this study. Here, the authors present a novel work (which is a proposed complementary portion of the overall solution to the deep network classification explainability problem for this application) by applying a systematic progression of preprocessing and a deep neural network (ResNet architecture) to classify UCE data obtained via current transformers. By using a methodical application of preprocessing techniques prior to a deep classifier, hypotheses can be produced concerning what features the deep network deems important relative to what it perceives as noise. For instance, it is hypothesized in this particular study as a result of execution of the proposed method and periodic inspection of the classifier output that the UCE spectral features are relatively close to each other or to the interferers, as systematically reducing the beta parameter of the Kaiser window produced progressively better classification performance, but only to a point, as going below the Beta of eight produced decreased classifier performance, as well as the hypothesis that further spectral feature resolution was not as important to the classifier as rejection of the leakage from a spectrally distant interference. This can be very important in unpredictable low-FNR applications, where knowing the difference between features and noise is difficult. As a side-benefit, much was learned regarding the best preprocessing to use with the selected deep network for the UCE collected from these low power consumer devices obtained via current transformers. Baseline rectangular windowed FFT preprocessing provided a 62% classification increase versus using raw samples. After performing a more optimal preprocessing, more than 90% classification accuracy was achieved across 18 low-power consumer devices for scenarios in which the in-band features-to-noise ratio (FNR) was very poor.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Harmonic Signals Dataset

The Harmonic Signals Dataset (HSD) consists of one-dimensional time series data collected from current sensors with a goal of providing data for research and development of Non-Intrusive Load Monitoring (NILM) with high sample rate (800kHz) sensors. NILM seeks to detect and characterize electrical equipment operating within a facility by sensing changes in electrical current on the building power system associated with the equipment. To provide data with a known ground truth, but with the realism of a signal introduced within a building facility, a series of known ground truth signals were injected as voltage into the power system of a building and measurements of the signals at multiple locations across the building power system were made with electrical current sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

TPSAS-NF1676L-12156-DND

The IVHM project recommends nine priorities for future IVHM research. Strain measurement technology is one of the nine recommendations. The Decadal Survey of Civil Aeronautics: Foundation for the Future, identified IVHM as the top NASA and national priority within the area of materials and structures. The survey also identified IVHM systems that warrant attention over the next decade such as “locally self-powered, wireless microelectromechanical sensors of various types tiny enough that very large numbers of sensors become practical.” Standard strain gages have been used to detect cracks in aircraft, and have been used to monitor load conditions and fatigue in airframes. High temperature devices will be able to monitor structural health in areas that are inaccessible today.

Cy Wilson↗
DOE OSTI2025

Low-Cost Identification and Monitoring of Diverse MELs in Residential and Commercial Buildings with PowerBlade (Final Technical Report)

This report describes our progress in developing a scalable wireless metering system that integrates plug-load energy meters that measure real, reactive, and apparent power along with load monitoring based on extracting high-fidelity electrical waveform features collected at every load to capture power profiles and automatically identify and categorize MELs, and make this technology available to the research, commercial, and government sectors.

47 OTHER INSTRUMENTATION↗