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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Integrating Oil Debris and Vibration Measurements for Intelligent Machine Health Monitoring

A diagnostic tool for detecting damage to gears was developed. Two different measurement technologies, oil debris analysis and vibration were integrated into a health monitoring system for detecting surface fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabilities as compared to using individual measurement technologies. This diagnostic tool was developed and evaluated experimentally by collecting vibration and oil debris data from fatigue tests performed in the NASA Glenn Spur Gear Fatigue Rig. An oil debris sensor and the two vibration algorithms were adapted as the diagnostic tools. An inductance type oil debris sensor was selected for the oil analysis measurement technology. Gear damage data for this type of sensor was limited to data collected in the NASA Glenn test rigs. For this reason, this analysis included development of a parameter for detecting gear pitting damage using this type of sensor. The vibration data was used to calculate two previously available gear vibration diagnostic algorithms. The two vibration algorithms were selected based on their maturity and published success in detecting damage to gears. Oil debris and vibration features were then developed using fuzzy logic analysis techniques, then input into a multi sensor data fusion process. Results show combining the vibration and oil debris measurement technologies improves the detection of pitting damage on spur gears. As a result of this research, this new diagnostic tool has significantly improved detection of gear damage in the NASA Glenn Spur Gear Fatigue Rigs. This research also resulted in several other findings that will improve the development of future health monitoring systems. Oil debris analysis was found to be more reliable than vibration analysis for detecting pitting fatigue failure of gears and is capable of indicating damage progression. Also, some vibration algorithms are as sensitive to operational effects as they are to damage. Another finding was that clear threshold limits must be established for diagnostic tools. Based on additional experimental data obtained from the NASA Glenn Spiral Bevel Gear Fatigue Rig, the methodology developed in this study can be successfully implemented on other geared systems.

Dempsey, Paula J.↗
NASA NTRS1993

Driving a car with custom-designed fuzzy inferencing VLSI chips and boards

Vehicle control in a-priori unknown, unpredictable, and dynamic environments requires many calculational and reasoning schemes to operate on the basis of very imprecise, incomplete, or unreliable data. For such systems, in which all the uncertainties can not be engineered away, approximate reasoning may provide an alternative to the complexity and computational requirements of conventional uncertainty analysis and propagation techniques. Two types of computer boards including custom-designed VLSI chips were developed to add a fuzzy inferencing capability to real-time control systems. All inferencing rules on a chip are processed in parallel, allowing execution of the entire rule base in about 30 microseconds, and therefore, making control of 'reflex-type' of motions envisionable. The use of these boards and the approach using superposition of elemental sensor-based behaviors for the development of qualitative reasoning schemes emulating human-like navigation in a-priori unknown environments are first discussed. Then how the human-like navigation scheme implemented on one of the qualitative inferencing boards was installed on a test-bed platform to investigate two control modes for driving a car in a-priori unknown environments on the basis of sparse and imprecise sensor data is described. In the first mode, the car navigates fully autonomously, while in the second mode, the system acts as a driver's aid providing the driver with linguistic (fuzzy) commands to turn left or right and speed up or slow down depending on the obstacles perceived by the sensors. Experiments with both modes of control are described in which the system uses only three acoustic range (sonar) sensor channels to perceive the environment. Simulation results as well as indoors and outdoors experiments are presented and discussed to illustrate the feasibility and robustness of autonomous navigation and/or safety enhancing driver's aid using the new fuzzy inferencing hardware system and some human-like reasoning schemes which may include as little as six elemental behaviors embodied in fourteen qualitative rules.

Pin, Francois G.↗
NASA NTRS2013

Smarter Earth Science Data System

The explosive growth in Earth observational data in the recent decade demands a better method of interoperability across heterogeneous systems. The Earth science data system community has mastered the art in storing large volume of observational data, but it is still unclear how this traditional method scale over time as we are entering the age of Big Data. Indexed search solutions such as Apache Solr (Smiley and Pugh, 2011) provides fast, scalable search via keyword or phases without any reasoning or inference. The modern search solutions such as Googles Knowledge Graph (Singhal, 2012) and Microsoft Bing, all utilize semantic reasoning to improve its accuracy in searches. The Earth science user community is demanding for an intelligent solution to help them finding the right data for their researches. The Ontological System for Context Artifacts and Resources (OSCAR) (Huang et al., 2012), was created in response to the DARPA Adaptive Vehicle Make (AVM) programs need for an intelligent context models management system to empower its terrain simulation subsystem. The core component of OSCAR is the Environmental Context Ontology (ECO) is built using the Semantic Web for Earth and Environmental Terminology (SWEET) (Raskin and Pan, 2005). This paper presents the current data archival methodology within a NASA Earth science data centers and discuss using semantic web to improve the way we capture and serve data to our users.

data center↗
NASA NTRS1979

Lunar laser ranging data deposited in the National Space Science Data Center normal points, filtered observations, and unfiltered photon detections

The lunar laser ranging project at McDonald Observatory provides the unique opportunity to acquire successfully precise range data for the earth-moon system. From the experiment's inception, the obligation was recognized to make these data available to the general scientific community in a reasonably useable form and in a realistic time frame. The documentation to be used in conjunction with the 1979 April deposit into the National Space Science Data Center which contains normal points, filtered observations and unfiltered photon stops for the months July through December, 1978 are reported.

Shelus, P. J.↗
NASA NTRS1987

Nitrous oxide in the tropical middle atmosphere, observed by ground-based mm-wave spectrometry

Measurements of stratospheric N2O were made from Mauna Kea in Hawaii in June 1983, and in May and June 1986, by observing thermal emission of the molecule in a rotational transition at about 1 mm wavelength. Analyses of the data yield altitude profiles in the middle and upper stratosphere. Useful measurements of N2O may be made in one to two hours. The N2O profiles agree reasonably well with model predictions and with published satellite data, though significantly more N2O is reported near the stratopause than shown by the satellite measurement, and significantly more N2O in the middle stratosphere than in one of the models. The discrepancy between these data and the satellite measurement may be due in part to variations induced by the solar cycle.

Connor, Brian J.↗
NASA NTRS1996

LET spectra of trapped anomalous cosmic rays in low-Earth orbit

Observations aboard Cosmos satelites discovered trapped anomalous cosmic rays (ACRs), tracked the variation in their intensity in 1986-1988, and measured their fluence, spectrum, and composition at solar minimum in the previous solar cycle. The MAST instrument aboard the SAMPEX satellite has observed trapped anomalous cosmic rays in the present solar cycle, confirmed the general features of the Cosmos data, and provided the first detailed observations of trapped ACRs. In this paper we apply theoretical modeling of trapped ACRs, which is shown to provide a reasonably good description of both the Cosmos and SAMPEX data, to calculate the integral linear-energy-transfer (LET) spectra due to trapped ACRs in typical low-Earth orbits. We compare these calculations with the LET spectra produced by galactic cosmic rays (GCRs) and non-trapped ACRs in order to assess the relative radiation hazard posed by trapped ACRs.

Tylka, A. J.↗
NASA NTRS1989

An airborne study of microwave surface sensing and boundary layer heat and moisture fluxes for Project FIFE

This is the third semiannual status report describing the University of Wyoming participation in project FIFE, under NASA Grant NAG 5-913. The reader is referred to the first and second semiannual status reports for this grant for descriptions of University of Wyoming participation in the field operations of 1987. This report is concerned with continuing analysis of field data. Most of the instrument calibration effort has concentrated on interpretation and use of the high-rate water vapor measurements with the University of Wyoming Lyman-alpha device. In addition, fluxes of water vapor have been corrected for sensible heat flux, and all the high-rate (10 Hz for the Wyoming King Air) data have been filtered before the flux calculations. For each flight the Lyman-alpha data (10 Hz) are calibrated against the Cambridge dew-point hygrometer (1 Hz), by using data from the take-off sounding. The reasoning here is that the takeoff sounding (held to 500 fpm rate-of-climb specifically for the calibration) covers the widest range of humidity conditions of any sequence of the flight. For each takeoff sounding, then, the Lyman-alpha data, averaged to 1 Hz, are matched to the dewpoint data with a second-order least-squares fit. The resulting quadratic equation is then used in all subsequent analyses to convert the Lyman-alpha voltages to vapor desities for that specific flight. An alternative method is to use the range of fluctuation for the same two devices from horizontal passes to establish the calibration function. However, since the Lyman-alpha data are used only in the flux calculations, i.e., only the fluctuation and not the absolute values are used, this method is thought unnecessary.

Kelly, Robert D.↗
NASA NTRS1990

Supplemental knowledge acquisition through external product interface for CLIPS

Traditionally, the acquisition of knowledge for expert systems consisted of the interview process with the domain or subject matter expert (SME), observation of domain environment, and information gathering and research which constituted a direct form of knowledge acquisition (KA). The knowledge engineer would be responsible for accumulating pertinent information and/or knowledge from the SME(s) for input into the appropriate expert system development tool. The direct KA process may (or may not) have included forms of data or documentation to incorporate from the SME's surroundings. The differentiation between direct KA and supplemental KA (indirect) would be the difference in the use of data. In acquiring supplemental knowledge, the knowledge engineer would access other types of evidence (manuals, documents, data files, spreadsheets, etc.) that would support the reasoning or premises of the SME. When an expert makes a decision in a particular task, one tool that may have been used to justify a recommendation, would have been a spreadsheet total or column figure. Locating specific decision points from that data within the SME's framework would constitute supplemental KA. Data used for a specific purpose in one system or environment would be used as supplemental knowledge for another, specifically a CLIPS project.

Saito, Tim↗
NASA NTRS2008

Description of the NASA Hypobaric Decompression Sickness Database (1982-1998)

The availability of high-speed computers, data analysis software, and internet communication are compelling reasons to describe and make available computer databases from many disciplines. Methods: Human research using hypobaric chambers to understand and then prevent decompression sickness (DCS) during space walks has been conducted at the Johnson Space Center (JSC) from 1982 to 1998. The data are archived in the NASA Hypobaric Decompression Sickness Database, within an Access 2003 Relational Database. Results: There are 548 records from 237 individuals that participated in 31 unique tests. Each record includes physical characteristics, the denitrogenation procedure that was tested, and the outcome of the test, such as the report of a DCS symptom and the intensity of venous gas emboli (VGE) detected with an ultrasound Doppler bubble detector as they travel in the venous blood along the pulmonary artery on the way to the lungs. We documented 84 cases of DCS and 226 cases where VGE were detected. The test altitudes were 10.2, 10.1, 6.5, 6.0, and 4.3 pounds per square inch absolute (psia). 346 records are from tests conducted at 4.3 psia, the operating pressure of the current U.S. space suit. 169 records evaluate the Staged 10.2 psia Decompression Protocol used by the Space Shuttle Program. The mean exposure time at altitude was 242.3 minutes (SD = 80.6), with a range from 120 to 360 minutes. Among our test subjects, 96 records of exposures are females. The mean age of all test subjects was 31.8 years (SD = 7.17), with a range from 20 to 54 years. Discussion: These data combined with other published databases and evaluated with metaanalysis techniques would extend our understanding about DCS. A better understanding about the cause and prevention of DCS would benefit astronauts, aviators, and divers.

Wessel, J. H., III↗
NASA NTRS2021

Relationship between Horizontal Wind Velocity and Normalized Surface Cross Section Using Data from the HIWRAP Dual-Frequency Airborne Radar

The High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) dual-frequency conically scanning airborne radar provides estimates of the range-profiled mean Doppler and backscattered power from the precipitation and surface. A velocity–azimuth display analysis yields near-surface estimates of the mean horizontal wind vector υh in cases in which precipitation is present throughout the scan. From the surface return, the normalized radar cross section (NRCS) is obtained, which, by a method previously described, can be corrected for path attenuation. Comparisons between υh and the attenuation-corrected NRCS are used to derive transfer functions that provide estimates of the wind vector from the NRCS data under both rain and rain-free conditions. A reasonably robust transfer function is found by using the mean NRCS (⟨NRCS⟩) over the scan along with a filtering of the data based on a Fourier series analysis of υh and the NRCS. The approach gives good correlation coefficients between υh and ⟨NRCS⟩ at Ku band at incidence angles of 30° and 40°. The correlation degrades if the Ka-band data are used rather than the Ku band.

R Meneghini↗
NASA NTRS1974

The broadening of He I lines including ion dynamic corrections, with application to 4471 A

A theory of the broadening of He I lines with forbidden components is developed. The theory takes into account the effects of dynamic ion broadening and is valid at low densities where the forbidden line is reasonably well isolated. Good agreement with experiment and with calculations by Lee (1972) was obtained when the theory was applied at a wavelength of 4471 A. Tables of these data are given and are extended into the line wings beyond the extent of previous tabulations. Reasons for agreement with Lee data are discussed in detail. The density level above which the static ion theory is adequate is defined. A simple numerical profile of allowed lines is also obtained, taking into account dynamic ion effects.

Barnard, A. J.↗
NASA NTRS1979

Case studies of gravity waves associated with isolated tornadic storms on January 13, 1976

Penetrative convection, thunderstorms, squall lines, etc., all generate atmospheric gravity waves which can be observed by a ground-based ionospheric Doppler sounder array. Sources of these waves can be determined from reverse ray tracing computations. Case studies of gravity waves associated with isolated tornadic storms on January 13, 1976 were summarized to establish the minimum data sampling time required for correct spectral analysis and ray tracing computations. It was concluded that the data sampling time can be reduced to two to three times the wave period while still obtaining a reasonably good power spectral density. It was also demonstrated that the data sampling time can be reduced to two to three times the time delay of the wave arrival between two station pairs while still obtaining a justifiably good cross-spectral analysis. Computed source locations of the observed gravity waves are compared with conventional and satellite meteorological data.

Hung, R. J.↗
NASA NTRS1993

Applications of artificial intelligence 1993: Knowledge-based systems in aerospace and industry; Proceedings of the Meeting, Orlando, FL, Apr. 13-15, 1993

The present volume on applications of artificial intelligence with regard to knowledge-based systems in aerospace and industry discusses machine learning and clustering, expert systems and optimization techniques, monitoring and diagnosis, and automated design and expert systems. Attention is given to the integration of AI reasoning systems and hardware description languages, care-based reasoning, knowledge, retrieval, and training systems, and scheduling and planning. Topics addressed include the preprocessing of remotely sensed data for efficient analysis and classification, autonomous agents as air combat simulation adversaries, intelligent data presentation for real-time spacecraft monitoring, and an integrated reasoner for diagnosis in satellite control. Also discussed are a knowledge-based system for the design of heat exchangers, reuse of design information for model-based diagnosis, automatic compilation of expert systems, and a case-based approach to handling aircraft malfunctions.

Fayyad, Usama M.↗
NASA NTRS2015

A Description of the Development, Capabilities, and Operational Status of the Test SLATE Data Acquisition System at the National Transonic Facility

The paper will present a brief background of the previous data acquisition system at the National Transonic Facility (NTF) and the reasoning and goals behind the upgrade to the current Test SLATE (Test Software Laboratory and Automated Testing Environments) data acquisition system. The components, performance characteristics, and layout of the Test SLATE system within the NTF control room will be discussed. The development, testing, and integration of Test SLATE within NTF operations will be detailed. The operational capabilities of the system will be outlined including: test setup, instrumentation calibration, automatic test sequencer setup, data recording, communication between data and facility control systems, real time display monitoring, and data reduction. The current operational status of the Test SLATE system and its performance during recent NTF testing will be highlighted including high-speed, frame-by-frame data acquisition with conditional sampling post-processing applied. The paper concludes with current development work on the system including the capability for real-time conditional sampling during data acquisition and further efficiency enhancements to the wind tunnel testing process.

Cramer, Christopher J.↗
NASA NTRS1986

STAR - A computer language for hybrid AI applications

Constructing Artificial Intelligence application systems which rely on both symbolic and non-symbolic processing places heavy demands on the communication of data between dissimilar languages. This paper describes STAR (Simple Tool for Automated Reasoning), a computer language for the development of AI application systems which supports the transfer of data structures between a symbolic level and a non-symbolic level defined in languages such as FORTRAN, C and PASCAL. The organization of STAR is presented, followed by the description of an application involving STAR in the interpretation of airborne imaging spectrometer data.

Borchardt, G. C.↗
NASA NTRS2007

A Contrast in Use of Metrics in Earth Science Data Systems

In recent years there has been a surge in the number of systems for processing, archiving and distributing remotely sensed data. Such systems, working independently as well as in collaboration, have been contributing greatly to the advances in the scientific understanding of the Earth system, as well as utilization of the data for nationally and internationally important applications. Among such systems, we consider those that are developed by or under the sponsorship of NASA to fulfill one of its strategic objectives: "Study Earth from space to advance scientific understanding and meet societal needs." NASA's Earth science data systems are of varying size and complexity depending on the requirements they are intended to meet. Some data systems are regarded as NASA's "Core Capabilities" that provide the basic infrastructure for processing, archiving and distributing a set of data products to a large and diverse user community in a robust and reliable manner. Other data systems constitute "Community Capabilities". These provide specialized and innovative services to data users and/or research products offering new scientific insight. Such data systems are generally supported by NASA through peer reviewed competition. Examples of Core Capabilities are 1. Earth Observing Data and Information System (EOSDIS) with its Distributed Active Archive Centers (DAACs), Science Investigator-led Processing Systems (SIPSs), and the EOS Clearing House (ECHO); 2. Tropical Rainfall Measurement Mission (TRMM) Science Data and Information System (TSDIS); 3. Ocean Data Processing System (ODPS); and 4. CloudSat Data Processing Center. Examples of Community Capabilities are projects under the Research, Education and Applications Solutions Network (REASON), and Advancing Collaborative Connections for Earth System Science (ACCESS) Programs. In managing these data system capabilities, it is necessary to have well-established goals and to measure progress relative to them. Progress is measured through "metrics", which can be a combination of quantitative as well as qualitative assessments. The specific metrics of interest depend on the user of the metrics as well as the type of data system. The users of metrics can be data system managers, program managers, funding agency or the public. Data system managers need metrics for assessing and improving the performance of the system and for future planning. Program managers need metrics to assess progress and the value of the data systems sponsored by them. Also, there is a difference in the metrics needed for core capabilities that tend to be more complex, larger and longer-term compared to community capabilities and the community capabilities that tend to be simpler, smaller and shorter-term. Even among community capabilities there are differences; hence the same set of metrics does not apply to all. Some provide data products to users, some provide services that enable better utilization of data or interoperability among other systems, and some are a part of a larger project where provision of data or services is only a minor activity. There is also a contrast between metrics used for internal and external purposes. Examples of internal purposes are: ensuring that the system meets its requirements, and planning for evolution and growth. Examples of external purposes are: providing to sponsors indicators of success of the systems, demonstrating the contributions of the system to overall program success, etc. This paper will consider EOSDIS, REASON and ACCESS programs to show the various types of metrics needed and how they need to be tailored to the types of data systems while maintaining the overall management goals of measuring progress and contributions made by the data systems.

Ramapriyan, Hampapuram↗
NASA NTRS1985

Development of an unsteady aerodynamics model to improve correlation of computed blade stresses with test data

A reliable rotor aeroelastic analysis operational that correctly predicts the vibration levels for a helicopter is utilized to test various unsteady aerodynamics models with the objective of improving the correlation between test and theory. This analysis called Rotor Aeroelastic Vibration (RAVIB) computer program is based on a frequency domain forced response analysis which utilizes the transfer matrix techniques to model helicopter/rotor dynamic systems of varying degrees of complexity. The results for the AH-1G helicopter rotor were compared with the flight test data during high speed operation and they indicated a reasonably good correlation for the beamwise and chordwise blade bending moments, but for torsional moments the correlation was poor. As a result, a new aerodynamics model based on unstalled synthesized data derived from the large amplitude oscillating airfoil experiments was developed and tested.

Gangwani, S. T.↗
NASA NTRS1995

Interaction of the terrestrial and atmospheric hydrological cycles in the context of the North American southwest summer monsoon

Work under this grant has used information on precipitation and water vapor fluxes in the area of the Mexican Monsoon to analyze the regional precipitation climatology, to understand the nature of water vapor transport during the monsoon using model and observational data, and to analyze the ability of the TRMM remote sensing algorithm to characterize precipitation. An algorithm for estimating daily surface rain volumes from hourly GOES infrared images was developed and compared to radar data. Estimates were usually within a factor of two, but different linear relations between satellite reflectances and rainfall rate were obtained for each day, storm type and storm development stage. This result suggests that using TRMM sensors to calibrate other satellite IR will need to be a complex process taking into account all three of the above factors. Another study, this one of the space-time variability of the Mexican Monsoon, indicate that TRMM will have a difficult time, over the course of its expected three year lifetime, identifying the diurnal cycle of precipitation over monsoon region. Even when considering monthly rainfalls, projected satellite estimates of August rainfall show a root mean square error of 38 percent. A related examination of spatial variability of mean monthly rainfall using a novel method for removing the effects of elevation from gridded gauge data, show wide variation from a satellite-based rainfall estimates for the same time and space resolution. One issue addressed by our research, relating to the basic character of the monsoon circulation, is the determination of the source region for moisture. The monthly maps produced from our study of monsoon variability show the presence of two rainfall maxima in the analysis normalized to sea level, one in south-central Arizona associated with the Mexican monsoon maximum and one in southeastern New Mexico associated with the Gulf of Mexico. From the point of view of vertically-integrated fluxes and flux divergence of water vapor from ECMWF data, most moisture at upper levels arrives from the Gulf of Mexico, while low level moisture comes from the northern Gulf of California. Composites of ECMWF analyses for wet and dry periods (classified by rain gauge data) show that both regimes show low level moisture arriving from northern and central Gulf of California. Above 700 MB, moisture comes from both source regions and the Sierra Madre Occidental. During wet periods a longer fetch through the moist air mass above western Mexico results in a greater moisture flux into the Sonoran Desert region, while there is less moisture from the Gulf of Mexico both above and below 700 mb. Work on the grant subcontract at the University of Colorado concentrated on the development of a technique useful to TRMM combining visible, infrared and passive microwave data for measuring precipitation. Two established techniques using either visible or infrared data applied over the US Southwest correlated with gauges at the 0.58 to 0.70 level. The application of some established passive microwave techniques were less successful for a variety of reason, including problems in both the gauge and satellite data quality, sampling problems and weaknesses inherent in the algorithms themselves. A more promising solution for accurate rainfall estimation was explored using visible and infrared data to perform a cloud classification, which when combined with information about the background (e.g. Iand/ocean), was used to select the most appropriate microwave algorithm from a suite of possibilities.

Dickinson, Robert E.↗