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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

propulsion system performance↗
NASA NTRS2014

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

propulsion system performance↗
NASA NTRS1972

Fabrication and evaluation of a weak zone plate for monitoring performance of large orbiting telescopes

An experimental study of the feasibility of monitoring the optical performance of a large telescope by means of a very faint phase hologram imprinted upon the primary mirror is reported. Tests have been made using an f/5 telescope with a 0.3 m aperture. The results indicate that a usable hologram can be so faint and so restricted in area that it will probably not interfere significantly with normal operation of a 3.0 m telescope at wavelengths longer than 100 nm for stars brighter than magnitude 29.

Erickson, K. E.↗
NASA NTRS2000

Autonomous Performance Monitoring System: Monitoring and Self-Tuning (MAST)

Maintaining the long-term performance of software onboard a spacecraft can be a major factor in the cost of operations. In particular, the task of controlling and maintaining a future mission of distributed spacecraft will undoubtedly pose a great challenge, since the complexity of multiple spacecraft flying in formation grows rapidly as the number of spacecraft in the formation increases. Eventually, new approaches will be required in developing viable control systems that can handle the complexity of the data and that are flexible, reliable and efficient. In this paper we propose a methodology that aims to maintain the accuracy of flight software, while reducing the computational complexity of software tuning tasks. The proposed Monitoring and Self-Tuning (MAST) method consists of two parts: a flight software monitoring algorithm and a tuning algorithm. The dependency on the software being monitored is mostly contained in the monitoring process, while the tuning process is a generic algorithm independent of the detailed knowledge on the software. This architecture will enable MAST to be applicable to different onboard software controlling various dynamics of the spacecraft, such as attitude self-calibration, and formation control. An advantage of MAST over conventional techniques such as filter or batch least square is that the tuning algorithm uses machine learning approach to handle uncertainty in the problem domain, resulting in reducing over all computational complexity. The underlying concept of this technique is a reinforcement learning scheme based on cumulative probability generated by the historical performance of the system. The success of MAST will depend heavily on the reinforcement scheme used in the tuning algorithm, which guarantees the tuning solutions exist.

Peterson, Chariya↗
NASA NTRS1998

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Trejo, Leonard J.↗
NASA NTRS1999

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Evoked Potentials↗
NASA NTRS1998

Observations from Long-Term Performance Monitoring of the TOPEX Radar Altimeter

From its August 1992 launch to the present, we have performed performance analysis and engineering assessment for the TOPEX radar altimeter. We continually update databases containing: (1) on-board engineering parameters such as temperatures, voltages, and currents; (2) internal calibration mode indications of bias changes in range and power estimates; and (3) global over-ocean averages of geophysical quantities such as significant waveheight (SWH) and ocean surface radar backscattering cross-section (sigmaO). For some time now we have seen an apparent increase in the TOPEX cycle-average SWH, amounting now to an increase of about 10% from our earlier mission value of about 2.8 m. Recently several investigators have reported that the TOPEX SWH is increasing relative to ERS-2 and to ocean buoy measurements. We examined the past six years of waveform data from an internal calibration mode (Cal 1) which samples the altimeter's point target response (ptr), and found that the ptr sidelobes are apparently greater now than at start of mission. Modeling studies indicate that the sidelobe enhancement can account for most of the increase in the TOPEX SWH estimate; that is, the SWH increase is not real but result of some drift within the altimeter. Our modeling indicates that the TOPEX range estimates will change less than a centimeter for ptr changes seen to date. An approximate recipe for correcting the TOPEX SWH values will be made available on our web site (http://topex.wffnasa.gov/). We emphasize that a radar altimeter is a complex system requiring continuing calibration and monitoring throughout its entire lifetime.

Hayne, G. S.↗
NASA NTRS1985

Monitoring Performance of Complex Systems

33-page report describes use of computers in automatic on-line monitoring of Centaur rocket prior to launch. Computers analyze measurements and verify events and commands. System uses adaptive software so only real problems are detected and brought to attention of engineers. Software techniques transferable to such industrial uses as batch process control and production line automation.

Thomas, W. F.↗
DOE OSTI2026

Database Performance Monitoring for DUNE

This report presents the research, design, and implementation of improved PostgreSQL monitoring for DUNE Rucio database services using Checkmk. The project began with a request to improve dashboard visibility for database performance metrics, including connection usage, configured connection limits, lock activity, wait behavior, storage trends, query performance, and saturation alerts. The initial implementation focused on the dune_rucio_prod database on the rucio_prod PostgreSQL instance because connection saturation and lock contention are direct reliability risks for database-backed services. Existing Checkmk PostgreSQL monitoring was investigated, and several gaps were identified. Built-in connection monitoring did not clearly separate active, idle, idle-in-transaction, total, and usage-percent metrics, while the built-in lock monitoring simplified PostgreSQL lock modes into shared and exclusive categories. To address these gaps, two DSG-specific Checkmk local checks were created: one for connection-state monitoring and one for lock-state monitoring. These checks supplement the built-in PostgreSQL checks and provide additional performance data for dashboard graphs, service states, and alerts.

Bowers, Elliot [Cabrillo Coll.]↗
DOE OSTI2025

REDESIGNING A PERFORMANCE MONITORING SOFTWARE FOR SUPERCOMPUTERS

The objective of this project was to improve upon the existing Watchr software that charts performance test metrics from the Trilinos project run on supercomputers at Sandia and elsewhere across the DOE complex. Software was iteratively designed and developed using Python Pandas and Dash data visualization to improve the extensibility and user experience of Watchr. Documentation is being maintained for future developers who want to extend the application.

Camacho, Dane Joseph [Sandia National Laboratories↗
NASA NTRS2024

A High-Performance Computing Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

This report offers analysis and design insights for leveraging High-Performance Computing (HPC) to predict line-of-sight (LOS) Global Navigation Satellite System (GNSS) availability in a city. This work is motivated by the emerging fields of Advanced and Urban Air Mobility (AAM/UAM), where regulatory authorities are seeking city-scale, meter-resolution risk forecasting in order to safely integrate new flight missions with existing urban life and infrastructure. This work addresses the technical challenge of efficiently computing urban GNSS satellite visibility to predict GNSS performance metrics under these requirements. We present a new HPC-optimized shadow casting algorithm variant as a ray-based approach to forecasting satellite visibility. We apply this algorithm variant in a software-defined prognostic service which generates a GNSS navigation risk-correlated map as a path planning-style potential field. We detail dominant computational burdens, viable simplifying assumptions, and different algorithmic implementations, intending to demonstrate a baseline of computation time needed by each stage in such a service. We conclude by analyzing the prototype service’s prediction accuracy compared to receiver data from Corpus Christi, Texas. This informs design trade-offs along the dimensions of hardware, computation time, and tolerable forecasting error (including proportions of false positives and false negatives).

GNSS↗
NASA NTRS2016

Digital Image Correlation for Performance Monitoring

Evaluating the health of a mechanism requires more than just a binary evaluation of whether an operation was completed. It requires analyzing more comprehensive, full-field data. Health monitoring is a process of non-destructively identifying characteristics that indicate the fitness of an engineered component. In order to monitor unit health in a production setting, an automated test system must be created to capture the motion of mechanism parts in a real-time and non-intrusive manner. One way to accomplish this is by using high-speed video and Digital Image Correlation (DIC). In this approach, individual frames of the video are analyzed to track the motion of mechanism components. The derived performance metrics allow for state-of-health monitoring and improved fidelity of mechanism modeling. The results are in-situ state-of-health identification and performance prediction. This paper introduces basic concepts of this test method, and discusses two main themes: the use of laser marking to add fiducial patterns to mechanism components, and new software developed to track objects with complex shapes, even as they move behind obstructions. Finally, the implementation of these tests into an automated tester is discussed.

Palaviccini, Miguel↗
DOE OSTI2025

Embedded Sensing in Additive Manufacturing Metal and Polymer Parts: A Comparative Study of Integration Techniques and Structural Health Monitoring Performance

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications.

additive manufacturing↗
DOE OSTI2023

Health Assessment and Performance Monitoring of Large Machine Diagnostics

Diagnostic machines are a crucial component of the Stockpile Stewardship Program for data collection, toward the ultimate goal of improving our understanding of nuclear physics. These systems consist of interactions between many complex components, and require regular maintenance for acceptable performance. The Cygnus X-ray diagnostic located at the Nevada National Security Site's U1a underground facility which provides radiographic data for subcritical experiments, is a prime example of these systems. Component degradation and failures within Cygnus can result in system downtime and data loss, affecting schedules and increasing experiment costs. To evade such failures, years worth of data on machine performance has been collected on the two Cygnus axes in the form of voltage and current measurements of Cygnus' various components. Using these as input, we have developed machine learning techniques for assessing the health of Cygnus, with the ultimate goal of predicting declining performance and machine failure.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

A real-time diagnostic and performance monitor for UNIX

There are now over one million UNIX sites and the pace at which new installations are added is steadily increasing. Along with this increase, comes a need to develop simple efficient, effective and adaptable ways of simultaneously collecting real-time diagnostic and performance data. This need exists because distributed systems can give rise to complex failure situations that are often un-identifiable with single-machine diagnostic software. The simultaneous collection of error and performance data is also important for research in failure prediction and error/performance studies. This paper introduces a portable method to concurrently collect real-time diagnostic and performance data on a distributed UNIX system. The combined diagnostic/performance data collection is implemented on a distributed multi-computer system using SUN4's as servers. The approach uses existing UNIX system facilities to gather system dependability information such as error and crash reports. In addition, performance data such as CPU utilization, disk usage, I/O transfer rate and network contention is also collected. In the future, the collected data will be used to identify dependability bottlenecks and to analyze the impact of failures on system performance.

Dong, Hongchao↗
NASA NTRS2015

Suomi-NPP VIIRS Solar Diffuser Stability Monitor Performance

When illuminated by the Sun, the onboard solar diffuser (SD) panel provides a known spectral radiance source to calibrate the reflective solar bands of the Visible Infrared Imaging Radiometer Suite on the Suomi-NPP satellite. The SD bidirectional reflectance distribution function (BRDF) degrades over time due to solar exposure, and this degradation is measured using the SD stability monitor (SDSM). The SDSM acts as a ratioing radiometer, comparing solar irradiance measurements off the SD panel to those from a direct Sun view. We discuss the design and operations of the SDSM, the SDSM data analysis, including improvements incorporated since launch, and present the results through 1000 days after launch. After 1000 days, the band-dependent H-factors, a quantity describing the relative degradation of the BRDF of the SD panel since launch, range from 0.716 at 412 nanometers to 0.989 at 926 nanometers. The random uncertainty of these H-factors is about 0.1 percent, which is confirmed by the similar standard deviation values computed from the residuals of quadratic exponential fits to the H-factor time trends. The SDSM detector gains have temperature sensitivity of up to about 0.36 percent per kelvin, but this does not affect the derived H-factors. An initial error in the solar vector caused a seasonal bias to the H-factors of up to 0.5 percent. The total exposure of the SD panel to UV light after 1000 orbits is equivalent to about 100 hours of direct sunlight illumination perpendicular to the SD panel surface.

Fulbright, Jon↗
DOE OSTI2022

Health Assessment and Performance Monitoring of Large Machine Diagnostics

Presentation to be given at the CCS-6 Statistical Seminar Series. Regularly maintained and operated diagnostic machines are a backbone of data collection and are a vital component of the Stockpile Stewardship Program’s efforts to better understand nuclear physics. The Cygnus X-ray machine, stationed at the Nevada National Security Site’s U1a underground facility, is one such diagnostic that provides a radiographic capability for the subcritical experiment program. Component failures within Cygnus can result in catastrophic downtime for the diagnostic, affecting performance, schedules, and cost. However, over the years various measurements have been collected on the two Cygnus axes, including voltage and current measurements at different locations, which we believe have predictive power to indicate machine health. We will share preliminary insight into this data and the machine learning approaches we are taking to assess Cygnus’ health, observe declining performance, and predict failures.

97 MATHEMATICS AND COMPUTING↗