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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Development of a knowledge acquisition tool for an expert system flight status monitor

Two of the main issues in artificial intelligence today are knowledge acquisition and knowledge representation. The Dryden Flight Research Facility of NASA's Ames Research Center is presently involved in the design and implementation of an expert system flight status monitor that will provide expertise and knowledge to aid the flight systems engineer in monitoring today's advanced high-performance aircraft. The flight status monitor can be divided into two sections: the expert system itself and the knowledge acquisition tool. This paper discusses the knowledge acquisition tool, the means it uses to extract knowledge from the domain expert, and how that knowledge is represented for computer use. An actual aircraft system has been codified by this tool with great success. Future real-time use of the expert system has been facilitated by using the knowledge acquisition tool to easily generate a logically consistent and complete knowledge base.

Disbrow, J. D.↗
NASA NTRS1992

Automating software design system DESTA

'DESTA' is the acronym for the Dialogue Evolutionary Synthesizer of Turnkey Algorithms by means of a natural language (Russian or English) functional specification of algorithms or software being developed. DESTA represents the computer-aided and/or automatic artificial intelligence 'forgiving' system which provides users with software tools support for algorithm and/or structured program development. The DESTA system is intended to provide support for the higher levels and earlier stages of engineering design of software in contrast to conventional Computer Aided Design (CAD) systems which provide low level tools for use at a stage when the major planning and structuring decisions have already been taken. DESTA is a knowledge-intensive system. The main features of the knowledge are procedures, functions, modules, operating system commands, batch files, their natural language specifications, and their interlinks. The specific domain for the DESTA system is a high level programming language like Turbo Pascal 6.0. The DESTA system is operational and runs on an IBM PC computer.

Lovitsky, Vladimir A.↗
NASA NTRS1986

Development of a knowledge acquisition tool for an expert system flight status monitor

Two of the main issues in artificial intelligence today are knowledge acquisition dion and knowledge representation. The Dryden Flight Research Facility of NASA's Ames Research Center is presently involved in the design and implementation of an expert system flight status monitor that will provide expertise and knowledge to aid the flight systems engineer in monitoring today's advanced high-performance aircraft. The flight status monitor can be divided into two sections: the expert system itself and the knowledge acquisition tool. The knowledge acquisition tool, the means it uses to extract knowledge from the domain expert, and how that knowledge is represented for computer use is discussed. An actual aircraft system has been codified by this tool with great success. Future real-time use of the expert system has been facilitated by using the knowledge acquisition tool to easily generate a logically consistent and complete knowledge base.

Disbrow, J. D.↗
NASA NTRS1991

EXODUS: Integrating intelligent systems for launch operations support

Kennedy Space Center (KSC) is developing knowledge-based systems to automate critical operations functions for the space shuttle fleet. Intelligent systems will monitor vehicle and ground support subsystems for anomalies, assist in isolating and managing faults, and plan and schedule shuttle operations activities. These applications are being developed independently of one another, using different representation schemes, reasoning and control models, and hardware platforms. KSC has recently initiated the EXODUS project to integrate these stand alone applications into a unified, coordinated intelligent operations support system. EXODUS will be constructed using SOCIAL, a tool for developing distributed intelligent systems. EXODUS, SOCIAL, and initial prototyping efforts using SOCIAL to integrate and coordinate selected EXODUS applications are described.

Adler, Richard M.↗
NASA NTRS1986

Development of an expert system prototype for determining software functional requirements for command management activities at NASA Goddard

The development of an expert system prototype for software functional requirement determination for NASA Goddard's Command Management System, as part of its process of transforming general requests into specific near-earth satellite commands, is described. The present knowledge base was formulated through interactions with domain experts, and was then linked to the existing Knowledge Engineering Systems (KES) expert system application generator. Steps in the knowledge-base development include problem-oriented attribute hierarchy development, knowledge management approach determination, and knowledge base encoding. The KES Parser and Inspector, in addition to backcasting and analogical mapping, were used to validate the expert system-derived requirements for one of the major functions of a spacecraft, the solar Maximum Mission. Knowledge refinement, evaluation, and implementation procedures of the expert system were then accomplished.

Liebowitz, J.↗
NASA NTRS1986

Knowledge based programming at KSC

Various KSC knowledge-based systems projects are discussed. The objectives of the knowledge-based automatic test equipment and Shuttle connector analysis network projects are described. It is observed that knowledge-based programs must handle factual and expert knowledge; the characteristics of these two types of knowledge are examined. Applications for the knowledge-based programming technique are considered.

Tulley, J. H., Jr.↗
NASA NTRS1992

Intelligent fault management for the Space Station active thermal control system

The Thermal Advanced Automation Project (TAAP) approach and architecture is described for automating the Space Station Freedom (SSF) Active Thermal Control System (ATCS). The baseline functionally and advanced automation techniques for Fault Detection, Isolation, and Recovery (FDIR) will be compared and contrasted. Advanced automation techniques such as rule-based systems and model-based reasoning should be utilized to efficiently control, monitor, and diagnose this extremely complex physical system. TAAP is developing advanced FDIR software for use on the SSF thermal control system. The goal of TAAP is to join Knowledge-Based System (KBS) technology, using a combination of rules and model-based reasoning, with conventional monitoring and control software in order to maximize autonomy of the ATCS. TAAP's predecessor was NASA's Thermal Expert System (TEXSYS) project which was the first large real-time expert system to use both extensive rules and model-based reasoning to control and perform FDIR on a large, complex physical system. TEXSYS showed that a method is needed for safely and inexpensively testing all possible faults of the ATCS, particularly those potentially damaging to the hardware, in order to develop a fully capable FDIR system. TAAP therefore includes the development of a high-fidelity simulation of the thermal control system. The simulation provides realistic, dynamic ATCS behavior and fault insertion capability for software testing without hardware related risks or expense. In addition, thermal engineers will gain greater confidence in the KBS FDIR software than was possible prior to this kind of simulation testing. The TAAP KBS will initially be a ground-based extension of the baseline ATCS monitoring and control software and could be migrated on-board as additional computation resources are made available.

Hill, Tim↗
NASA NTRS1991

Applying knowledge compilation techniques to model-based reasoning

Researchers in the area of knowledge compilation are developing general purpose techniques for improving the efficiency of knowledge-based systems. In this article, an attempt is made to define knowledge compilation, to characterize several classes of knowledge compilation techniques, and to illustrate how some of these techniques can be applied to improve the performance of model-based reasoning systems.

Keller, Richard M.↗
NASA NTRS1998

Use of microgravity sensors for quantification of space shuttle orbiter vernier reaction control system induced environments

In the modeling of spacecraft dynamics it is important to accurately characterize the environment in which the vehicle operates, including the environments induced by the vehicle itself. On the Space Shuttle these induced environmental factors include reaction control system plume. Knowledge of these environments is necessary for performance of control systems and loads analyses, estimation of disturbances due to thruster firings, and accurate state vector propagation. During the STS-71 mission, while the Orbiter was performing attitude control for the mated Orbiter/Mir stack, it was noted that the autopilot was limit cycling at a rate higher than expected from pre-flight simulations. Investigations during the mission resulted in the conjecture that an unmodelled plume impingement force was acting upon the orbiter elevons. The in-flight investigations were not successful in determining the actual magnitude of the impingement, resulting in several sequential post-flight investigations. Efforts performed to better quantify the vernier reaction control system induced plume impingement environment of the Space Shuttle orbiter are described in this paper, and background detailing circumstances which required the more detailed knowledge of the RCS self impingement forces, as well as a description of the resulting investigations and their results is presented. The investigations described in this paper applied microgravity acceleration data from two shuttle borne microgravity experiments, SAMS and OARE, to the solution of this particular problem. This solution, now used by shuttle analysts and mission planners, results in more accurate propellant consumption and attitude limit cycle estimates in preflight analyses, which are critical for pending International Space Station missions.

Friend, Robert B.↗
NASA NTRS1992

An expert system shell for inferring vegetation characteristics

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. The report describes the extensions that have been made to the first generation version of VEG. An interface to a file of unkown cover type data has been constructed. An interface that allows the results of VEG to be written to a file has been implemented. A learning system that learns class descriptions from a data base of historical cover type data and then uses the learned class descriptions to classify an unknown sample has been built. This system has an interface that integrates it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER has been completed and a number of additional techniques that infer the proportion ground cover of a sample have been implemented.

Harrison, P. Ann↗
NASA NTRS1993

An expert system shell for inferring vegetation characteristics

The NASA VEGetation Workbench (VEG) is a knowledge based system that infers vegetation characteristics from reflectance data. VEG is described in detail in several references. The first generation version of VEG was extended. In the first year of this contract, an interface to a file of unknown cover type data was constructed. An interface that allowed the results of VEG to be written to a file was also implemented. A learning system that learned class descriptions from a data base of historical cover type data and then used the learned class descriptions to classify an unknown sample was built. This system had an interface that integrated it into the rest of VEG. The VEG subgoal PROPORTION.GROUND.COVER was completed and a number of additional techniques that inferred the proportion ground cover of a sample were implemented. This work was previously described. The work carried out in the second year of the contract is described. The historical cover type database was removed from VEG and stored as a series of flat files that are external to VEG. An interface to the files was provided. The framework and interface for two new VEG subgoals that estimate the atmospheric effect on reflectance data were built. A new interface that allows the scientist to add techniques to VEG without assistance from the developer was designed and implemented. A prototype Help System that allows the user to get more information about each screen in the VEG interface was also added to VEG.

Harrison, P. Ann↗
DOE OSTI2025

A Knowledge Graph Approach to Analyze Systems and Assets Health

Nuclear power plants collect large amounts of equipment reliability data elements that contain information on the statuses of component, assets, and systems. All these data elements precisely record asset and system performance and health throughout the lifecycle of those assets and systems. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly focuses on the integration of numeric and textual data elements in order to assist plant system engineers in analyzing equipment reliability data. This task begins with preprocessing the data by extracting knowledge from textual data via natural language processing methods and quantifying system, asset, and component health based on numeric data. We then employed model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Data elements were then associated with a single MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.

97 - MATHEMATICS AND COMPUTING↗
NASA NTRS2001

The Acquisition Process as a Vehicle for Enabling Knowledge Management in the Lifecycle of Complex Federal Systems

This white paper explores how to increase the success and operation of critical, complex, national systems by effectively capturing knowledge management requirements within the federal acquisition process. Although we focus on aerospace flight systems, the principles outlined within may have a general applicability to other critical federal systems as well. Fundamental design deficiencies in federal, mission-critical systems have contributed to recent, highly visible system failures, such as the V-22 Osprey and the Delta rocket family. These failures indicate that the current mechanisms for knowledge management and risk management are inadequate to meet the challenges imposed by the rising complexity of critical systems. Failures of aerospace system operations and vehicles may have been prevented or lessened through utilization of better knowledge management and information management techniques.

Stewart, Helen↗
NASA NTRSDate not supplied

Methods for Developing Successful Systems Engineers

Systems Engineering (SE) is a complex and challenging field that incorporates the knowledge of systems engineering processes, the ability to synthesize a wide-range of engineering disciplines, and the ability to lead a team of people to successfully accomplish the goals of a project. It requires hard technical skills and soft-skill leadership savvy. As a result, three main development needs are identified: 1) Knowledge of SE processes, the benefits of these processes to a project and their tailored application 2) Knowledge of a wide-range of engineering disciplines, how they interrelate in a system, and the development of sound technical judgement 3) Team leadership to direct and motivate a team of subsystem and discipline experts This paper describes the establishment of a comprehensive training and development program for Systems Engineers at NASA Ames Research Center that addresses in part each of these three areas from the perspective of the implementing manager. A variety of methods have been utilized including the establishment of a SE Community of Practice, a unique and innovative web tool, on-line videos, classroom training in NASA’s 17 Common Technical Processes, guidance on the tailored application of these processes, monthly technical talks, mentoring in both technical judgment and team leadership, and NASA’s Leadership Development Programs. While much of professional SE development must come through project experience, the approaches listed above can accelerate development. The diversity of skills required of Systems Engineering demands a multi-faceted approach to successfully train and develop this critical skill.

development↗
NASA NTRSDate not supplied

Methods for Developing Successful Systems Engineers

Systems Engineering (SE) is a complex and challenging field that incorporates the knowledge of systems engineering processes, the ability to synthesize a wide-range of engineering disciplines, and the ability to lead a team of people to successfully accomplish the goals of a project. It requires hard technical skills and soft-skill leadership savvy. As a result, three main development needs are identified: 1. Knowledge of SE processes, the benefits of these processes to a project and their tailored application 2. Knowledge of a wide-range of engineering disciplines, how they interrelate in a system, and the development of sound technical judgement 3. Team leadership to direct and motivate a team of subsystem and discipline experts This paper describes the establishment of a comprehensive training and development program for Systems Engineers at NASA Ames Research Center that addresses in part each of these three areas from the perspective of the implementing manager. A variety of methods have been utilized including the establishment of a SE Community of Practice, a unique and innovative web tool, on-line videos, classroom training in NASA’s 17 Common Technical Processes, guidance on the tailored application of these processes, monthly technical talks, mentoring in both technical judgment and team leadership, and NASA’s Leadership Development Programs. While much of professional SE development must come through project experience, the approaches listed above can accelerate development. The diversity of skills required of Systems Engineering demands a multi-faceted approach to successfully train and develop this critical skill.

development↗
NASA NTRS1983

Sensitivity analysis by approximation formulas - Illustrative examples

This paper examines the reliability of three architectures for six components. For each architecture, the probabilities of the failure states are given by algebraic formulas involving the component fault rate, the system recovery rate, and the operating time. The dominant failure modes are identified, and the change in reliability is considered with respect to changes in fault rate, recovery rate, and operating time. The major conclusions concern the influence of system architecture on failure modes and parameter requirements. Without this knowledge, a system designer may pick an inappropriate structure.

White, A. L.↗
NASA NTRS1991

Real Time Data System (RTDS)

Lessons learned from operational real time expert systems are examined. The basic system architecture is discussed. An expert system is any software that performs tasks to a standard that would normally require a human expert. An expert system implies knowledge contained in data rather than code. And an expert system implies the use of heuristics as well as algorithms. The 15 top lessons learned by the operation of a real time data system are presented.

Muratore, John F.↗
NASA NTRS1991

Advanced technologies for Mission Control Centers

Advance technologies for Mission Control Centers are presented in the form of the viewgraphs. The following subject areas are covered: technology needs; current technology efforts at GSFC (human-machine interface development, object oriented software development, expert systems, knowledge-based software engineering environments, and high performance VLSI telemetry systems); and test beds.

Dalton, John T.↗