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At least 109 records · Page 6

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NASA NTRS1993

GenSAA: A tool for advancing satellite monitoring with graphical expert systems

During numerous contacts with a satellite each day, spacecraft analysts must closely monitor real time data for combinations of telemetry parameter values, trends, and other indications that may signify a problem or failure. As satellites become more complex and the number of data items increases, this task is becoming increasingly difficult for humans to perform at acceptable performance levels. At the NASA Goddard Space Flight Center, fault-isolation expert systems have been developed to support data monitoring and fault detection tasks in satellite control centers. Based on the lessons learned during these initial efforts in expert system automation, a new domain-specific expert system development tool named the Generic Spacecraft Analyst Assistant (GenSAA) is being developed to facilitate the rapid development and reuse of real-time expert systems to serve as fault-isolation assistants for spacecraft analysts. Although initially domain-specific in nature, this powerful tool will support the development of highly graphical expert systems for data monitoring purposes throughout the space and commercial industry.

Hughes, Peter M.↗

NASA NTRS1993

System of experts for intelligent data management (SEIDAM)

A proposal to conduct research and development on a system of expert systems for intelligent data management (SEIDAM) is being developed. CCRS has much expertise in developing systems for integrating geographic information with space and aircraft remote sensing data and in managing large archives of remotely sensed data. SEIDAM will be composed of expert systems grouped in three levels. At the lowest level, the expert systems will manage and integrate data from diverse sources, taking account of symbolic representation differences and varying accuracies. Existing software can be controlled by these expert systems, without rewriting existing software into an Artificial Intelligence (AI) language. At the second level, SEIDAM will take the interpreted data (symbolic and numerical) and combine these with data models. at the top level, SEIDAM will respond to user goals for predictive outcomes given existing data. The SEIDAM Project will address the research areas of expert systems, data management, storage and retrieval, and user access and interfaces.

Goodenough, David G.↗

NASA NTRS1996

The feasibility study and evaluation of applying expert system techniques to the mission operations for the AXAF-I spacecraft

Advanced X-ray Astrophysics Facility - Imaging (AXAF-I) is a spacecraft for X-ray emitting sources observation and has been tentatively scheduled for a space shuttle launch in late 1998 at the Kennedy Space Center. Its main objectives are 'to determine the nature of astronomical objects ranging from normal stars to quasars, to understand the nature of the physical processes which take place in and between astronomical objects, and to add to our understanding of the history and evolution of the universe.' The AXAF-I will have an expected five year life time for the science mission phase. During the science mission phase, the monitoring and management operation of the flight and ground systems is personnel intensive, requiring system experts on duty around the clock. The purpose of the expert system presented in this report is intended to reduce the level of expertise, training, and personnel requirement for the mission operation. The telemetry data from the spacecraft can be divided into two categories: the science observation data and the engineering status data. The science data contains the outputs from the X-ray sensing devices and will be forwarded to the AXAF-I Science Center for interpretation; while the engineering status data will be monitored by the Operation Control Center (OCC) for the operation diagnosis of the spacecraft. The expert system is designed to assist the operation controllers at the OCC to perform the daily mission operations. Since there are hundreds of engineering telemetry data points and the interpretation of the telemetry depends on many factors, e.g., sun or eclipse, the monitoring of the AXAF-I is not a trivial task. In this phase of expert system development, the focus has been limited to the engineering data interpretation, i.e., warnings will be provided to the operation controllers to signal any anomaly. The system is hosted in a Silicon Graphics Indigo-2 workstation running the IRIX operating system. The expert system tool used is the G2 system from Gensym (Gensy).

Chang, Kai H.↗

NASA NTRS1998

System and method for creating expert systems

A system and method provides for the creation of a highly graphical expert system without the need for programming in code. An expert system is created by initially building a data interface, defining appropriate Mission, User-Defined, Inferred, and externally-generated GenSAA (EGG) data variables whose data values will be updated and input into the expert system. Next, rules of the expert system are created by building appropriate conditions of the rules which must be satisfied and then by building appropriate actions of rules which are to be executed upon corresponding conditions being satisfied. Finally, an appropriate user interface is built which can be highly graphical in nature and which can include appropriate message display and/or modification of display characteristics of a graphical display object, to visually alert a user of the expert system of varying data values, upon conditions of a created rule being satisfied. The data interface building, rule building, and user interface building are done in an efficient manner and can be created without the need for programming in code.

Hughes, Peter M.↗

NASA NTRS2008

Expert System Control of Plant Growth in an Enclosed Space

The Expert System is an enclosed, controlled environment for growing plants, which incorporates a computerized, knowledge-based software program that is designed to capture the knowledge, experience, and problem-solving skills of one or more human experts in a particular discipline. The Expert System is trained to analyze crop/plant status, to monitor the condition of the plants and the environment, and to adjust operational parameters to optimize the plant-growth process. This system is intended to provide a way to remotely control plant growth with little or no human intervention. More specifically, the term control implies an autonomous method for detecting plant states such as health (biomass) or stress and then for recommending and implementing cultivation and/or remediation to optimize plant growth and to minimize consumption of energy and nutrients. Because of difficulties associated with delivering energy and nutrients remotely, a key feature of this Expert System is its ability to minimize this effort and to achieve optimum growth while taking into account the diverse range of environmental considerations that exist in an enclosed environment. The plant-growth environment for the Expert System could be made from a variety of structures, including a greenhouse, an underground cavern, or another enclosed chamber. Imaging equipment positioned within or around the chamber provides spatially distributed crop/plant-growth information. Sensors mounted in the chamber provide data and information pertaining to environmental conditions that could affect plant development. Lamps in the growth environment structure supply illumination, and other additional equipment in the chamber supplies essential nutrients and chemicals.

May, George↗

NASA NTRS2018

Using Machine-Learning Methods and Expert Prediction Probabilities to Forecast Solar Flares

It has long been known that studying connection between solar flares and properties of magnetic field in active regions is very important for understanding the flare physics and developing space weather forecasts. The Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) obtains tremendous amounts of magnetic field data products. However the operational NOAA Space Weather Prediction Center (SWPC) forecasts of solar flares still represent prediction probabilities issued by the experts. In this research we investigate the possibilities to enhance the daily operational flare forecasts performed at the SWPC by developing a synergy of the expert predictions and physics-based criteria, and by employing machine-learning methods. Among the physics-based criteria we consider the descriptors of the Polarity Inversion Line (PIL) and Space weather HMI Active Region Patches (SHARP), and derive from them daily characteristics of the entire Sun. We also consider the daily descriptors of the GOES Soft X-Ray (SXR) 1-8 Angstroms flux such as the flare history of the previous days and averaged X-Ray flux. We estimate the effectiveness in separation of flaring and non-flaring cases for each characteristic, as well as for the expert prediction probabilities, and find that some PIL, SHARP and SXR descriptors are as effective as the expert prediction probabilities and should be considered to issue the flare forecast. Finally, we train and test several Machine-Learning classification algorithms (Support Vector Classifiers with various kernel functions, k-Nearest Neighbor Classifier, Random Forest Classifier, and Neural Networks) using the most effective descriptors and expert prediction probabilities, and compare the obtained predictions with the current SWPC forecasts.

Machine-Learning↗

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 NTRS1986

An engineering approach to the use of expert systems technology in avionics applications

The concept of using a knowledge compiler to transform the knowledge base and inference mechanism of an expert system into a conventional program is presented. The need to accommodate real-time systems requirements in applications such as embedded avionics is outlined. Expert systems and a brief comparison of expert systems and conventional programs are reviewed. Avionics applications of expert systems are discussed before the discussions of applying the proposed concept to example systems using forward and backward chaining.

Duke, E. L.↗

NASA NTRS1986

Design criteria for expert systems

Knowledge based expert systems are applicable to a wide range of engineering problems ranging from formation to derivation. At the formation end of the spectrum, design, planning and prediction have been identified as generic tasks with similar issues that are dealt with by experts, and need to be formalized for successful expert system implementation. At the derivation end, diagnosis, interpretation and monitoring have been identified as generic tasks with similar subproblems with which experts must cope. At theimplementation level, four levels of programming have been identified: logic programming, production system programming, object oriented programming and hybrid programming.

Allen, R.↗

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 NTRS1985

Feasibility of expert systems to enhance Space Station subsystem controllers

Results are described from a project to build a prototype expert system for automated fault isolation and correction of a regenerative CO2 removal device that is typical of functions of the air revitalization group in the Space Station environmental control and life support system (ECLSS). The software was developed using one of the powerful commercial knowledge engineering environments. The goal of the project was to evaluate the feasibility of using a software development environment to rapidly design, construct, test, and change expert system software. The use of expert systems to enhance automatic controllers and the use of information on device design and on device troubleshooting and repair procedures in developing expert systems are discussed.

Malin, J. T.↗

NASA NTRS1985

Application of expert systems in the Common Module electrical power system

Each Common Module (CM) of the Space Station must be capable of handling a 50 kW electricity supply, 25 kW for transmission and 25 kW for consumption. The power must be handled and managed by on-board systems, a necessity that dovetails with the objectives of Public Law 98-371, which mandates that the Space Station push the state of the art of automation and AI. Expert systems will be needed to handle the large data flow for the power system and to ensure that the system degrades gracefully. Features of the first expert systems expected for the power system, i.e., a dynamic load planner/scheduler and energy storage subsystem management, fault diagnosis/analysis, health status/trend analysis, and orbital replacement advisor expert systems, are described. Finally, growth Space Station expert systems applications are discussed.

Weeks, D. J.↗

NASA NTRS1987

Using output to evaluate and refine rules in rule-based expert systems

The techniques described provide an effective tool which knowledge engineers and domain experts can utilize to help in evaluating and refining rules. These techniques have been used successfully as learning mechanisms in a prototype adaptive diagnostic expert system and are applicable to other types of expert systems. The degree to which they constitute complete evaluation/refinement of an expert system depends on the thoroughness of their use.

St.clair, D. C.↗

NASA NTRS1987

Applications of expert systems for satellite autonomy

Some aspects of the on-board application of expert systems in artificial satellites are discussed. The activities of the study, which include the implementation of two prototypes on a dedicated artificial intelligence machine, are described. The general implications of the experience are then discussed. These concern the interrelationship between the expert system and the architecture of the satellite and the expert system's impact on the mission definition phase of the satellite lifecycle. The main obstacles that need to be overcome before operational use of onboard expert systems can take place are discussed.

Ciarlo, A.↗

NASA NTRS1987

Expert system verification concerns in an operations environment

The Space Shuttle community is currently developing a number of knowledge-based tools, primarily expert systems, to support Space Shuttle operations. It is proposed that anticipating and responding to the requirements of the operations environment will contribute to a rapid and smooth transition of expert systems from development to operations, and that the requirements for verification are critical to this transition. The paper identifies the requirements of expert systems to be used for flight planning and support and compares them to those of existing procedural software used for flight planning and support. It then explores software engineering concepts and methodology that can be used to satisfy these requirements, to aid the transition from development to operations and to support the operations environment during the lifetime of expert systems. Many of these are similar to those used for procedural hardware.

Goodwin, Mary Ann↗

NASA NTRS1988

Heat exchanger demonstration expert system

A real-time expert system intended for detecting and diagnosing faults in a 20 kW microwave transmitter heat exchanger is described. The expert system was developed on a LISP machine, Incorporated (LMI), Lambda Plus computer using Process Intelligent Control (PICON) software. The Heat Exhanger Expert System was tested and debugged. Future applications and extensions of the expert system to transmitters, masers, and antenna subassemblies are discussed.

Bagby, D. G.↗

NASA NTRS1988

Optimization of low gravity materials processing experiments using expert systems

The use of an expert system for the control of materials processing experiments in a facility such as the Space Station provides a number of attractive features for insuring that certain critical process parameters can be used to optimize the productivity of the materials processing experiments. The proposed approach to implementation of an expert system utilizes a knowledge base of desired process characteristics which will provide the desired results. The knowledge base for each experiment will be created in conjunction with the scientific investigator in charge of the experiment. Improvements to the knowledge base will be expanded to include self learning sessions in ground based experiments in order to teach the expert system how to respond to perturbations in the process and update the knowledge base on what process change should be implemented in order to reach the desired end product. Design considerations using an on-line real time expert system such as PICON is studied. The types of experiments evaluated include several types of solidification experiments which will be performed on the Space Station.

Workman, Gary L.↗

NASA NTRS1988

An expert system for a distributed real-time trainer

The problem addressed by this expert system concerns the expansion of capability of a Real Time Trainer for the Spacelab flight crew. As requirements for more models or fidelity are placed upon the system, expansion is necessary. The simulator can be expanded using a larger processor or by going to a distributed system and expand by adding additional processors. The distributed system is preferable because it is more economical and can be expanded in a more incremental manner. An expert system was developed to evaluate modeling and timing capability within a real time training simulator. The expert system is based upon a distributed configuration. Components of the modeled system are control tasks, network tasks, emulator tasks, processors, displays, and a network. The distributed module expert system (DMES) allows the configuring of processors, tasks, display use, keyboard use, and selection of alternate methods to update the data buffer. Modules can be defined with execution occurring in a specific processor on a network. The system consists of a knowledge front end editor to interactively generate or update the knowledge base, an inference engine, a display module, and a recording module.

Purinton, Steven C.↗