SEARCH · Engineering Papers

Results for “Network monitoring”

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 541 records · Page 30

All sources NASA NTRS DOE OSTI

NASA NTRS2005

GGOS working group on ground networks and communications

Activities of this Working Group include the investigation of the status quo and the development of a plan for full network integration to support improvements in terrestrial reference frame establishment and maintenance, Earth orientation and gravity field monitoring, precision orbit determination, and other geodetic and gravimetric applications required for the long-term observation of global change. This integration process includes the development of a network of fundamental stations with as many co-located techniques as possible, with precisely determined intersystem vectors. This network would exploit the strengths of each technique and minimize the weaknesses where possible. This paper discusses the organization of the working group, the work done to date, and future tasks.

VLBI↗

NASA NTRS2014

Emerging Needs for Pervasive Passive Wireless Sensor Networks on Aerospace Vehicles

NASA is investigating passive wireless sensor technology to reduce instrumentation mass and volume in ground testing, air flight, and space exploration applications. Vehicle health monitoring systems (VHMS) are desired on all aerospace programs to ensure the safety of the crew and the vehicles. Pervasive passive wireless sensor networks facilitate VHMS on aerospace vehicles. Future wireless sensor networks on board aerospace vehicles will be heterogeneous and will require active and passive network systems. Since much has been published on active wireless sensor networks, this work will focus on the need for passive wireless sensor networks on aerospace vehicles. Several passive wireless technologies such as microelectromechanical systems MEMS, SAW, backscatter, and chipless RFID techniques, have all shown potential to meet the pervasive sensing needs for aerospace VHMS applications. A SAW VHMS application will be presented. In addition, application areas including ground testing, hypersonic aircraft and spacecraft will be explored along with some of the harsh environments found in aerospace applications.

Wilson, William C.↗

NASA NTRS1993

Neural Network Prediction of Aluminum-Lithium Weld Strengths from Acoustic Emission Amplitude Data

Acoustic Emission (AE) flaw growth activity was monitored in aluminum-lithium weld specimens from the onset tensile loading to failure. Data on actual ultimate strengths together with AE data from the beginning of loading up to 25 percent of the expected ultimate strength were used to train a backpropagation neural network to predict ultimate strengths. Architecturally, the fully interconnected network consisted of an input layer for the AE amplitude data, a hidden layer to accommodate failure mechanism mapping, and an output layer for ultimate strength prediction. The trained network was the applied to the prediction of ultimate strengths in the remaining six specimens. The worst case prediction error was found to be +2.6 percent.

Hill, Eric v. K.↗

NASA NTRS2018

How to Leverage the Power of SAR Observations for Forest Monitoring Systems

Earth observations from Synthetic Aperture Radar (SAR) can provide unique observations related to forest structure and condition. Furthermore, SAR has many potential applications in forest monitoring systems, particularly where clouds have impeded optical observations. Currently, there is a reliable, freely-available, provision of SAR datasets, such as Sentinel-1, and there are plans to have more observations in the near- future (NISAR, BIOMASS). Given SAR’s enhanced earth observation characteristics, there is broad interest in using SAR datasets for decision support systems, such as deforestation early warning systems. However, applications of SAR are still underutilized. What is preventing users from using SAR data in their decision support systems? This study documents the experiences and lessons learned from the SERVIR network on the main limitations of incorporating SAR datasets into existing forest monitoring systems. This research also focuses on the major technical and scientific barriers we experience and best practices to address them. The results of this study are part of the SERVIR- SilvaCarbon collaboration. The primary goal of this collaboration is to build capacity in the applied use of SAR for forest monitoring and biomass estimation. The products of this effort aim to start closing the gap between SAR-science and forest applications. We will also present results to generate applied-ready knowledge for SAR.

SAR↗

NASA NTRS1981

Logic-controlled occlusive cuff system

An occlusive cuff system comprises a pressure cuff and a source of regulated compressed gas feeding the cuff through an electrically operated fill valve. An electrically operated vent valve vents the cuff to the ambient pressure. The fill valve is normally closed and the vent valve is normally open. In response to an external start signal, a logic network opens the fill valve and closes the vent valve, thereby starting the pressurization cycle and a timer. A pressure transducer continuously monitors the pressure in the cuff. When the transducer's output equals a selected reference voltage, a comparator causes the logic network to close the fill valve. The timer, after a selected time delay, opens the vent valve to the ambient pressure, thereby ending the pressurization cycle.

Baker, J. T.↗

NASA NTRS2016

A Neural Network Approach to Infer Optical Depth of Thick Ice Clouds at Night

One of the roadblocks to continuously monitoring cloud properties is the tendency of clouds to become optically black at cloud optical depths (COD) of 6 or less. This constraint dramatically reduces the quantitative information content at night. A recent study found that because of their diffuse nature, ice clouds remain optically gray, to some extent, up to COD of 100 at certain wavelengths. Taking advantage of this weak dependency and the availability of COD retrievals from CloudSat, an artificial neural network algorithm was developed to estimate COD values up to 70 from common satellite imager infrared channels. The method was trained using matched 2007 CloudSat and Aqua MODIS data and is tested using similar data from 2008. The results show a significant improvement over the use of default values at night with high correlation. This paper summarizes the results and suggests paths for future improvement.

Minnis, P.↗

NASA NTRS2017

Importance of a Priori Vertical Ozone Profiles for TEMPO Air Quality Retrievals

Ozone (O3) is a toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address the limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME (Global Ozone Monitoring Experiment), GOME-2, and OMI (Ozone Monitoring Instrument). This algorithm is suggested to use a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB-Clim) O3 climatology). This study evaluates the TB-Clim dataset and model simulated O3 profiles, which could potentially serve as a priori O3 profile information in TEMPO retrievals, from near-real-time data assimilation model products (NASA GMAO's (Global Modeling and Assimilation Office) operational GEOS-5 (Goddard Earth Observing System, Version 5) FP (Forecast Products) model and reanalysis data from MERRA2 (Modern-Era Retrospective analysis for Research and Applications, Version 2)) and a full chemical transport model (CTM), GEOS-Chem. In this study, vertical profile products are evaluated with surface (0-2 kilometers) and tropospheric (0-10 kilometers) TOLNet (Tropospheric Ozone Lidar Network) observations and the theoretical impact of individual a priori profile sources on the accuracy of TEMPO O3 retrievals in the troposphere and at the surface are presented. Results indicate that while the TB-Clim climatological dataset can replicate seasonally-averaged tropospheric O3 profiles, model-simulated profiles from a full CTM resulted in more accurate tropospheric and surface-level O3 retrievals from TEMPO when compared to hourly and daily-averaged TOLNet observations. Furthermore, it is shown that when large surface O3 mixing ratios are observed, TEMPO retrieval values at the surface are most accurate when applying CTM a priori profile information compared to all other data products.

Priori↗

NASA NTRS1993

Knowledge engineering for temporal dependency networks as operations procedures

This paper presents a case study of the knowledge engineering process employed to support the Link Monitor and Control Operator Assistant (LMCOA). The LMCOA is a prototype system which automates the configuration, calibration, test, and operation (referred to as precalibration) of the communications, data processing, metric data, antenna, and other equipment used to support space-ground communications with deep space spacecraft in NASA's Deep Space Network (DSN). The primary knowledge base in the LMCOA is the Temporal Dependency Network (TDN), a directed graph which provides a procedural representation of the precalibration operation. The TDN incorporates precedence, temporal, and state constraints and uses several supporting knowledge bases and data bases. The paper provides a brief background on the DSN, and describes the evolution of the TDN and supporting knowledge bases, the process used for knowledge engineering, and an analysis of the successes and problems of the knowledge engineering effort.

Fayyad, Kristina E.↗

NASA NTRS1986

Satellite battery testing status

Because of the large numbers of satellite cells currently being tested and anticipated at the Naval Weapons Support Center (NAVWPNSUPPCEN) Crane, Indiana, satellite cell testing is being integrated into the Battery Test Automation Project (BTAP). The BTAP, designed to meet the growing needs for battery testing at the NAVWPNSUPPCEN Crane, will consist of several Automated Test Stations (ATSs) which monitor batteries under test. Each ATS will interface with an Automation Network Controller (ANC) which will collect test data for reduction.

Haag, R.↗

NASA NTRS1993

Process control and recovery in the Link Monitor and Control Operator Assistant

This paper describes our approach to providing process control and recovery functions in the Link Monitor and Control Operator Assistant (LMCOA). The focus of the LMCOA is to provide semi-automated monitor and control to support station operations in the Deep Space Network. The LMCOA will be demonstrated with precalibration operations for Very Long Baseline Interferometry on a 70-meter antenna. Precalibration, the task of setting up the equipment to support a communications link with a spacecraft, is a manual, time consuming and error-prone process. One problem with the current system is that it does not provide explicit feedback about the effects of control actions. The LMCOA uses a Temporal Dependency Network (TDN) to represent an end-to-end sequence of operational procedures and a Situation Manager (SM) module to provide process control, diagnosis, and recovery functions. The TDN is a directed network representing precedence, parallelism, precondition, and postcondition constraints. The SM maintains an internal model of the expected and actual states of the subsystems in order to determine if each control action executed successfully and to provide feedback to the user. The LMCOA is implemented on a NeXT workstation using Objective C, Interface Builder and the C Language Integrated Production System.

Lee, Lorrine↗

NASA NTRS1993

Operations automation using the Link Monitor and Control Operator Assistant

The Link Monitor and Control Operator Assistant (LMC OA) is a knowledge-based prototype system which uses AI techniques to provide semiautomated monitor and control functions to support operations of the Deep Space Network (DSN) 70-m antenna at the Goldstone Deep Space Communications Complex (DSCC). The manual and time-consuming process of configuring the 70-m antenna and its associated communications and processing equipment, known as precalibration, is an overhead activity; the time spent in precalibration is time which cannot be spent supporting actual mission operations. Therefore, the major goal of the LMC OA task is to demonstrate techniques that reduce precalibration time, decrease operations overhead, and increase the availability of this valuable and oversubscribed NASA resource. The LMC OA prototype was tested in a parallel, experimental mode at the Goldstone DSCC performing semiautomated precalibration using the actual operational equipment. This test demonstrated that a reduction of 40 percent in precalibration time can be achieved with the LMC OA prototype.

Lee, Lorrine F.↗

NASA NTRS1995

Detecting Faults By Use Of Hidden Markov Models

Frequency of false alarms reduced. Faults in complicated dynamic system (e.g., antenna-aiming system, telecommunication network, or human heart) detected automatically by method of automated, continuous monitoring. Obtains time-series data by sampling multiple sensor outputs at discrete intervals of t and processes data via algorithm determining whether system in normal or faulty state. Algorithm implements, among other things, hidden first-order temporal Markov model of states of system. Mathematical model of dynamics of system not needed. Present method is "prior" method mentioned in "Improved Hidden-Markov-Model Method of Detecting Faults" (NPO-18982).

Smyth, Padhraic J.↗

NASA NTRS1993

Impasse-driven tutoring for reactive skill acquisition

We are interested in developing effective performance-oriented training for the operation of systems that are used for monitor and control purposes. We have focused on one such system, the communications Link Monitor and Control (LMC) system used in NASA's Deep Space Network (DSN), which is a worldwide system for navigating, tracking and communicating with unmanned interplanetary spacecraft. The tasks in this domain are procedural in nature and require reactive, goal-oriented skills; we have previously described a cognitive model for problem solving that accounts for both novice and expert levels of behavior as well as how skill is acquired. Our cognitive modeling work in this task domain led us to make a number of predictions about tutoring that have influenced the design of the system described in this paper.

Hill, Randall W., Jr.↗

NASA NTRS1999

Intercomparison of Various Algorithms in Determining Convective and Stratiform Precipitation

The paper presents the intercomparison of various algorithms in determining the convective and stratiform precipitation in tropical convection. The latent heat released from precipitation is the driving mechanism for the general circulation of the atmosphere and exhibits different vertical profile in convective and stratiform regimes of the tropical convection. The primary goal of this paper is to evaluate the performance of the operational convective/stratiform algorithm that is employed by the NASA Tropical Rainfall Measuring Mission (TRMM) ground validation program. The operational algorithm is based on the texture of radar reflectivity field at constant altitude. The TRMM ground validation program continuously monitors the tropical convection through ground based radar and rain gauge network at selected sites around the tropics and generates the rainfall products including the convective/stratiform rainfall map that can readily be used by the TRMM satellite program. An evaluation and improvement of these rainfall products requires additional measurements that can be available through field campaigns. The data used in this study was collected during the second phase of the Texas Florida Underflights (TEFLUN-B) field campaign in East Central Florida, a primary site of the TRMM ground validation program. The TEFLUN-B field campaign was conducted in August and September 1998 in support of the NASA Tropical Rainfall Measuring Mission (TRMM) satellite observations. An objective of the TEFLUN-B field campaign was to obtain the ground based precipitation measurements of Florida convection at different temporal and spatial scales. The field campaign had various components of precipitation measurements including dual wavelength precipitation profiler, various types of disdrometers and rain gauges, collocated about 40 km southwest of Melbourne NEXRAD radar. In this study, we examine the profiler based reflectivity and Doppler velocity and the disdrometer based drop size distribution observations to evaluate the operational scanning radar (i.e. NEXRAD) based convective/stratiform algorithm.

Tokay, Ali↗

NASA NTRS2000

Upgrade of the Thermal Vacuum Data System at NASA/GSFC

The Goddard Space Flight Center's new thermal vacuum data acquisition system is a networked client-sever application that enables lab operations crews to monitor all tests from a central location. The GSFC thermal vacuum lab consists of eleven chambers in Building 7 and one chamber in Building 10. The new data system was implemented for several reasons. These included the need for centralized data collection, more flexible and easier to use operator interface, greater data accessibility, a reduction in testing time and cost, and increased payload and personnel safety. Additionally, a new data system was needed for year-2000 compliance. This paper discusses the incorporation of the Thermal Vacuum Data System (TVDS) within the thermal vacuum lab at GSFC, its features and capabilities and lessons learned in its implementation. Additional topics include off-center (Internet) capability for remote monitoring and the role of TVDS in the efforts to automate thermal vacuum chamber operations.

Palmer, John↗

NASA NTRS2000

Upgrade of The Thermal Vacuum Data System at NASA/GSFC

Palmer, John↗

NASA NTRS2002

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

Kobayashi, Takahisa↗

NASA NTRS2012

Application of Satellite Gravimetry for Water Resource Vulnerability Assessment

The force of Earth's gravity field varies in proportion to the amount of mass near the surface. Spatial and temporal variations in the gravity field can be measured via their effects on the orbits of satellites. The Gravity Recovery and Climate Experiment (GRACE) is the first satellite mission dedicated to monitoring temporal variations in the gravity field. The monthly gravity anomaly maps that have been delivered by GRACE since 2002 are being used to infer changes in terrestrial water storage (the sum of groundwater, soil moisture, surface waters, and snow and ice), which are the primary source of gravity variability on monthly to decadal timescales after atmospheric and oceanic circulation effects have been removed. Other remote sensing techniques are unable to detect water below the first few centimeters of the land surface. Conventional ground based techniques can be used to monitor terrestrial water storage, but groundwater, soil moisture, and snow observation networks are sparse in most of the world, and the countries that do collect such data rarely are willing to share them. Thus GRACE is unique in its ability to provide global data on variations in the availability of fresh water, which is both vital to life on land and vulnerable to climate variability and mismanagement. This chapter describes the unique and challenging aspects of GRACE terrestrial water storage data, examples of how the data have been used for research and applications related to fresh water vulnerability and change, and prospects for continued contributions of satellite gravimetry to water resources science and policy.

Rodell, Matthew↗