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At least 667 records · Page 37

All sources NASA NTRS DOE OSTI

NASA NTRS1989

Integration of perception and reasoning in fast neural modules

Artificial neural systems promise to integrate symbolic and sub-symbolic processing to achieve real time control of physical systems. Two potential alternatives exist. In one, neural nets can be used to front-end expert systems. The expert systems, in turn, are developed with varying degrees of parallelism, including their implementation in neural nets. In the other, rule-based reasoning and sensor data can be integrated within a single hybrid neural system. The hybrid system reacts as a unit to provide decisions (problem solutions) based on the simultaneous evaluation of data and rules. Discussed here is a model hybrid system based on the fuzzy cognitive map (FCM). The operation of the model is illustrated with the control of a hypothetical satellite that intelligently alters its attitude in space in response to an intersecting micrometeorite shower.

Fritz, David G.↗

NASA NTRS1996

Generically Used Expert Scheduling System (GUESS): User's Guide Version 1.0

This user's guide contains instructions explaining how to best operate the program GUESS, a generic expert scheduling system. GUESS incorporates several important features for a generic scheduler, including automatic scheduling routines to generate a 'first' schedule for the user, a user interface that includes Gantt charts and enables the human scheduler to manipulate schedules manually, diagnostic report generators, and a variety of scheduling techniques. The current version of GUESS runs on an IBM PC or compatible in the Windows 3.1 or Windows '95 environment.

Liebowitz, Jay↗

NASA NTRS1990

Artificial intelligence in the materials processing laboratory

Materials science and engineering provides a vast arena for applications of artificial intelligence. Advanced materials research is an area in which challenging requirements confront the researcher, from the drawing board through production and into service. Advanced techniques results in the development of new materials for specialized applications. Hand-in-hand with these new materials are also requirements for state-of-the-art inspection methods to determine the integrity or fitness for service of structures fabricated from these materials. Two problems of current interest to the Materials Processing Laboratory at UAH are an expert system to assist in eddy current inspection of graphite epoxy components for aerospace and an expert system to assist in the design of superalloys for high temperature applications. Each project requires a different approach to reach the defined goals. Results to date are described for the eddy current analysis, but only the original concepts and approaches considered are given for the expert system to design superalloys.

Workman, Gary L.↗

NASA NTRS1986

Implementation of a data management software system for SSME test history data

The implementation of a software system for managing Space Shuttle Main Engine (SSME) test/flight historical data is presented. The software system uses the database management system RIM7 for primary data storage and routine data management, but includes several FORTRAN programs, described here, which provide customized access to the RIM7 database. The consolidation, modification, and transfer of data from the database THIST, to the RIM7 database THISRM is discussed. The RIM7 utility modules for generating some standard reports from THISRM and performing some routine updating and maintenance are briefly described. The FORTRAN accessing programs described include programs for initial loading of large data sets into the database, capturing data from files for database inclusion, and producing specialized statistical reports which cannot be provided by the RIM7 report generator utility. An expert system tutorial, constructed using the expert system shell product INSIGHT2, is described. Finally, a potential expert system, which would analyze data in the database, is outlined. This system could use INSIGHT2 as well and would take advantage of RIM7's compatibility with the microcomputer database system RBase 5000.

Abernethy, Kenneth↗

NASA NTRS1985

Artificial intelligence - New tools for aerospace project managers

Artificial Intelligence (AI) is currently being used for business-oriented, money-making applications, such as medical diagnosis, computer system configuration, and geological exploration. The present paper has the objective to assess new AI tools and techniques which will be available to assist aerospace managers in the accomplishment of their tasks. A study conducted by Brown and Cheeseman (1983) indicates that AI will be employed in all traditional management areas, taking into account goal setting, decision making, policy formulation, evaluation, planning, budgeting, auditing, personnel management, training, legal affairs, and procurement. Artificial intelligence/expert systems are discussed, giving attention to the three primary areas concerned with intelligent robots, natural language interfaces, and expert systems. Aspects of information retrieval are also considered along with the decision support system, and expert systems for project planning and scheduling.

Moja, D. C.↗

NASA NTRS2007

Decision Support Systems for Launch and Range Operations Using Jess

The virtual test bed for launch and range operations developed at NASA Ames Research Center consists of various independent expert systems advising on weather effects, toxic gas dispersions and human health risk assessment during space-flight operations. An individual dedicated server supports each expert system and the master system gather information from the dedicated servers to support the launch decision-making process. Since the test bed is based on the web system, reducing network traffic and optimizing the knowledge base is critical to its success of real-time or near real-time operations. Jess, a fast rule engine and powerful scripting environment developed at Sandia National Laboratory has been adopted to build the expert systems providing robustness and scalability. Jess also supports XML representation of knowledge base with forward and backward chaining inference mechanism. Facts added - to working memory during run-time operations facilitates analyses of multiple scenarios. Knowledge base can be distributed with one inference engine performing the inference process. This paper discusses details of the knowledge base and inference engine using Jess for a launch and range virtual test bed.

Thirumalainambi, Rajkumar↗

NASA NTRS1990

Identification and detection of anomalies through SSME data analysis

The goal of the ongoing research described in this paper is to analyze real-time ground test data in order to identify patterns associated with the anomalous engine behavior, and on the basis of this analysis to develop an expert system which detects anomalous engine behavior in the early stages of fault development. A prototype of the expert system has been developed and tested on the high frequency data of two SSME tests, namely Test #901-0516 and Test #904-044. The comparison of our results with the post-test analyses indicates that the expert system detected the presence of the anomalies in a significantly early stage of fault development.

Pereira, Lisa↗

NASA NTRS1989

Computer technologies and institutional memory

NASA programs for manned space flight are in their 27th year. Scientists and engineers who worked continuously on the development of aerospace technology during that period are approaching retirement. The resulting loss to the organization will be considerable. Although this problem is general to the NASA community, the problem was explored in terms of the institutional memory and technical expertise of a single individual in the Man-Systems division. The main domain of the expert was spacecraft lighting, which became the subject area for analysis in these studies. The report starts with an analysis of the cumulative expertise and institutional memory of technical employees of organizations such as NASA. A set of solutions to this problem are examined and found inadequate. Two solutions were investigated at length: hypertext and expert systems. Illustrative examples were provided of hypertext and expert system representation of spacecraft lighting. These computer technologies can be used to ameliorate the problem of the loss of invaluable personnel.

Bell, Christopher↗

NASA NTRS1988

Knowledge-based diagnosis for aerospace systems

The need for automated diagnosis in aerospace systems and the approach of using knowledge-based systems are examined. Research issues in knowledge-based diagnosis which are important for aerospace applications are treated along with a review of recent relevant research developments in Artificial Intelligence. The design and operation of some existing knowledge-based diagnosis systems are described. The systems described and compared include the LES expert system for liquid oxygen loading at NASA Kennedy Space Center, the FAITH diagnosis system developed at the Jet Propulsion Laboratory, the PES procedural expert system developed at SRI International, the CSRL approach developed at Ohio State University, the StarPlan system developed by Ford Aerospace, the IDM integrated diagnostic model, and the DRAPhys diagnostic system developed at NASA Langley Research Center.

Atkinson, David J.↗

NASA NTRS1988

Knowledge-based diagnosis for aerospace systems

Atkinson, David↗

NASA NTRS1988

Automatic Detection of Electric Power Troubles (ADEPT)

ADEPT is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system, and is designed for two modes of operation: real-time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a Laser printer. This system consists of a simulated Space Station power module using direct-current power supplies for Solar arrays on three power busses. For tests of the system's ability to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three busses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modelling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base. A load scheduler and a fault recovery system are currently under development to support both modes of operation.

Wang, Caroline↗

NASA NTRS1988

Automatic Detection of Electric Power Troubles (ADEPT)

Automatic Detection of Electric Power Troubles (A DEPT) is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system. It is designed for two modes of operation: real time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a laser printer. This system consists of a simulated space station power module using direct-current power supplies for solar arrays on three power buses. For tests of the system's ablilty to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three buses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modeling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base.

Wang, Caroline↗

NASA NTRS1995

Automated Diagnosis Of Conditions In A Plant-Growth Chamber

Biomass Production Chamber Operations Assistant software and hardware constitute expert system that diagnoses mechanical failures in controlled-environment hydroponic plant-growth chamber and recommends corrective actions to be taken by technicians. Subjects of continuing research directed toward development of highly automated closed life-support systems aboard spacecraft to process animal (including human) and plant wastes into food and oxygen. Uses Microsoft Windows interface to give technicians intuitive, efficient access to critical data. In diagnostic mode, system prompts technician for information. When expert system has enough information, it generates recovery plan.

Clinger, Barry R.↗

NASA NTRS1995

Analysis/Design Tool

Excelerator II, developed by INTERSOLV, Inc., provides a complete environment for rules-based expert systems. The software incorporates NASA's C Language Integrated Production System (CLIPS), a shell for constructing expert systems. Excelerator II provides complex verification and transformation routines based on matching that is simple and inexpensive. *Excelerator II was sold to SELECT Software Tools in June 1997 and is now called SELECT Excelerator. SELECT has assumed full support and maintenance for the product line.

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NASA NTRS1987

NASA Systems Autonomy Demonstration Project - Development of Space Station automation technology

A 1984 Congressional expansion of the 1958 National Aeronautics and Space Act mandated that NASA conduct programs, as part of the Space Station program, which will yield the U.S. material benefits, particularly in the areas of advanced automation and robotics systems. Demonstration programs are scheduled for automated systems such as the thermal control, expert system coordination of Station subsystems, and automation of multiple subsystems. The programs focus the R&D efforts and provide a gateway for transfer of technology to industry. The NASA Office of Aeronautics and Space Technology is responsible for directing, funding and evaluating the Systems Autonomy Demonstration Project, which will include simulated interactions between novice personnel and astronauts and several automated, expert subsystems to explore the effectiveness of the man-machine interface being developed. Features and progress on the TEXSYS prototype thermal control system expert system are outlined.

Bull, John S.↗

NASA NTRS1989

ART/Ada design project, phase 1

An Ada-Based Expert System Building Tool Design Research Project was conducted. The goal was to investigate various issues in the context of the design of an Ada-based expert system building tool. An attempt was made to achieve a comprehensive understanding of the potential for embedding expert systems in Ada systems for eventual application in future projects. The current status of the project is described by introducing an operational prototype, ART/Ada. How the project was conducted is explained. The performance of the prototype is analyzed and compared with other related works. Future research directions are suggested.

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NASA NTRS1991

Automated information retrieval using CLIPS

Expert systems have considerable potential to assist computer users in managing the large volume of information available to them. One possible use of an expert system is to model the information retrieval interests of a human user and then make recommendations to the user as to articles of interest. At Cal Poly, a prototype expert system written in the C Language Integrated Production System (CLIPS) serves as an Automated Information Retrieval System (AIRS). AIRS monitors a user's reading preferences, develops a profile of the user, and then evaluates items returned from the information base. When prompted by the user, AIRS returns a list of items of interest to the user. In order to minimize the impact on system resources, AIRS is designed to run in the background during periods of light system use.

Raines, Rodney Doyle, III↗

NASA NTRS1990

The composite load spectra project

Probabilistic methods and generic load models capable of simulating the load spectra that are induced in space propulsion system components are being developed. Four engine component types (the transfer ducts, the turbine blades, the liquid oxygen posts and the turbopump oxidizer discharge duct) were selected as representative hardware examples. The composite load spectra that simulate the probabilistic loads for these components are typically used as the input loads for a probabilistic structural analysis. The knowledge-based system approach used for the composite load spectra project provides an ideal environment for incremental development. The intelligent database paradigm employed in developing the expert system provides a smooth coupling between the numerical processing and the symbolic (information) processing. Large volumes of engine load information and engineering data are stored in database format and managed by a database management system. Numerical procedures for probabilistic load simulation and database management functions are controlled by rule modules. Rules were hard-wired as decision trees into rule modules to perform process control tasks. There are modules to retrieve load information and models. There are modules to select loads and models to carry out quick load calculations or make an input file for full duty-cycle time dependent load simulation. The composite load spectra load expert system implemented today is capable of performing intelligent rocket engine load spectra simulation. Further development of the expert system will provide tutorial capability for users to learn from it.

Newell, J. F.↗