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The Robot Environment Expert System uses a hexidecimal tree data structure to model a complex robot environment where not only the robot arm moves, but also the robot itself and other objects may move. The hextree model allows dynamic updating, collision avoidance and path planning over time, to avoid moving objects.
Starr, a prototype expert system, is designed to monitor and model a space power system, recognize problem states, identify the failure, and recommend the proper action to be taken. The system was modeled on the autonomously managed power system (AMPS) breadboard at NASA-Marshall. An object-oriented approach was used for the Starr model.
A proof of concept expert system called Ground Terminal Expert (GTEX) was developed at The University of Akron in collaboration with NASA Lewis Research Center. The objective of GTEX is to aid in diagnosing data faults occurring with a digital ground terminal. This strategy can also be applied to the Very Small Aperture Terminal (VSAT) technology. An expert system which detects and diagnoses faults would enhance the performance of the VSAT by improving reliability and reducing maintenance time. GTEX is capable of detecting faults, isolating the cause and recommending appropriate actions. Isolation of faults is completed to board-level modules. A graphical user interface provides control and a medium where data can be requested and cryptic information logically displayed. Interaction with GTEX consists of user responses and input from data files. The use of data files provides a method of simulating dynamic interaction between the digital ground terminal and the expert system. GTEX as described is capable of both improving reliability and reducing the time required for necessary maintenance.
Expert systems technology has much to offer to the problem of astronomical data analysis, where large data volumes and sophisticated analysis goals have caused a variety of interesting problems to arise. The construction of a prototype expert system whose target domain is CCD image calibration, is reported. The prototype is designed to be extensible to different and more complex problems in a straighforward way, and to be largely independent of the details of the specific data analysis system which executes the plan it generates.
There are many applications which may be done by an expert system in real time, if the system is capable of real-time response. The LISP and PROLOG-based expert systems have typically been too slow for real-time response. This has led to an effort to use other languages, the development of fast pattern matching techniques and other methods of improving the speed of expert systems. Another approach to developing faster expert systems is to make use of the emerging parallel processing computer technology. A further use for parallelism is to allow reasonable response time for large knowledge bases. The size of knowledge bases may become as large as 20,000 chunks of knowledge (and more) in the near future in medical and space applications. This paper describes the use of parallel processing in the EMYCIN backward chained rule-based model.
An expert system is being developed to support vehicle anomaly diagnosis for the Hubble Space Telescope (HST). Following a study of safemode entry analyses, a prototype system was developed which reads engineering telemetry formats, and when a safemode event is detected, extracts telemetry from the downlink and writes it into a knowledge base for more detailed analyses. The prototype then summarizes vehicle events (limits exceeded, specific failures). This prototype, the Telemetry Analysis Logic for Operations Support (TALOS) uses the Lockheed Expert System (LES) shell, and includes over 1600 facts, 230 rules, and 27 goals. Although considered a prototype, it is already an operationally useful system. The history leading into the TALOS prototype will be discussed, an overview of the present TALOS system will be presented, and the role of the TALOS system in contingency planning will be delineated.
Methods for building real-time onboard expert systems were investigated, and the use of expert systems technology was demonstrated in improving the performance of current real-time onboard monitoring and fault diagnosis applications. The potential applications of the proposed research include an expert system environment allowing the integration of expert systems into conventional time-critical application solutions, a grammar for describing the discrete event behavior of monitoring and fault diagnosis systems, and their applications to new real-time hardware fault diagnosis and monitoring systems for aircraft.
The Nickel-Cadmium Battery Expert System (NICBES) is an expert system for fault diagnosis and advice of the nickel-cadmium batteries found in the Hubble Space Telescope (HST). The system application and security, equipment environment, and the program maintenance procedures are examined.
The Web-based Weather Expert System (WES) is a critical module of the Virtual Test Bed development to support 'go/no go' decisions for Space Shuttle operations in the Intelligent Launch and Range Operations program of NASA. The weather rules characterize certain aspects of the environment related to the launching or landing site, the time of the day or night, the pad or runway conditions, the mission durations, the runway equipment and landing type. Expert system rules are derived from weather contingency rules, which were developed over years by NASA. Backward chaining, a goal-directed inference method is adopted, because a particular consequence or goal clause is evaluated first, and then chained backward through the rules. Once a rule is satisfied or true, then that particular rule is fired and the decision is expressed. The expert system is continuously verifying the rules against the past one-hour weather conditions and the decisions are made. The normal procedure of operations requires a formal pre-launch weather briefing held on Launch minus 1 day, which is a specific weather briefing for all areas of Space Shuttle launch operations. In this paper, the Web-based Weather Expert System of the Intelligent Launch and range Operations program is presented.
An expert system which tunes a proportional-integral-derivative (PID) controller online for a single-input-single-output multiple-lag process with dead time is described. The expert system examines features of the previous transient responses and their corresponding sets of controller parameters. It determines a new set of controller gains to obtain a more desirable time response. This technique can be used to determine and implement a different set of PID gains for each operating regime and, once in steady state, the system can be used to find optimal parameters for load disturbance rejection. The expert system can be applied to any system of the specified form (aerospace, industrial, etc.) and can be expanded to include additional process models.
An expert system which tunes a Proportional-Integral-Derivative (PID) controller on-line for a single-input-single-output multiple-lag process with dead time is described. The expert system examines features of the previous transient responses and their corresponding sets of controller parameters. It determines a new set of controller gains to obtain a more desirable time response. This technique can be used to determine and implement a different set of PID gains for each operating regime and, once in steady state, the system can be used to find optimal parameters for load disturbance rejection. The expert system can be applied to any system of the specified form (aerospace, industrial, etc.) and can be expanded to include additional process models.
Howard University, under the auspices of the Center for Energy Systems and Controls runs the Energy Expert Systems Institute (EESI) summer outreach program for high school/pre-college minority students. The main objectives are to introduce precollege minority students to research in the power industry using modern state-of-the-art technology such as Expert Systems, Fuzzy Logic and Artificial Neural Networks; to involve minority students in space power management, systems and failure diagnosis; to generate interest in career options in electrical engineering; and to experience problem-solving in a teamwork environment consisting of faculty, senior research associates and graduate students. For five weeks the students are exposed not only to the exciting experience of college life, but also to the inspiring field of engineering, especially electrical engineering. The program consists of lectures in the fundamentals of engineering, mathematics, communication skills and computer skills. The projects are divided into mini and major. Topics for the 1995 mini projects were Expert Systems for the Electric Bus and Breast Cancer Detection. Topics on the major projects include Hybrid Electric Vehicle, Solar Dynamics and Distribution Automation. On the final day, designated as 'EESI Day' the students did oral presentations of their projects and prizes were awarded to the best group. The program began in the summer of 1993. The reaction from the students has been very positive. The program also arranges field trips to special places of interest such as the NASA Goddard Space Center.
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.
As part of its evaluation of new technologies, the Artificial Intelligence Section of the Mission Planning and Analysis Div. at NASA-Johnson has made timing tests of several expert system building tools. Among the production systems tested were Automated Reasoning Tool, several versions of OPS5, and CLIPS (C Language Integrated Production System), an expert system builder developed by the AI section. Also included in the test were a Zetalisp version of the benchmark along with four versions of the benchmark written in Knowledge Engineering Environment, an object oriented, frame based expert system tool. The benchmarks used for testing are studied.
The research project results in a powerful yet user friendly CROPCAST expert system for use by a client to determine the crop yield production of a certain crop field. The study is based on the facts that heuristic assessment and decision making in agriculture are significant and dominate much of agribusiness. Transfer of the expert knowledge concerning remote sensing based crop yield production into a specific expert system is the key program in this study. A knowledge base consisting of a root frame, CROP-YIELD-FORECAST, and four subframes, namely, SATELLITE, PLANT-PHYSIOLOGY, GROUND, and MODEL were developed to accommodate the production rules obtained from the domain expert. The expert system shell Personal Consultant Plus version 4.0. was used for this purpose. An external geographic program was integrated to the system. This project is the first part of a completely built expert system. The study reveals that much effort was given to the development of the rules. Such effort is inevitable if workable, efficient, and accurate rules are desired. Furthermore, abundant help statements and graphics were included. Internal and external display routines add to the visual capability of the system. The work results in a useful tool for the client for making decisions on crop yield production.
When engineers diagnose system failures, they often use models to confirm system operation. This concept has produced a class of advanced expert systems that perform model-based diagnosis. A model-based diagnostic expert system for the Space Station Freedom electrical power distribution test bed is currently being developed at the NASA Lewis Research Center. The objective of this expert system is to autonomously detect and isolate electrical fault conditions. Marple, a software package developed at TRW, provides a model-based environment utilizing constraint suspension. Originally, constraint suspension techniques were developed for digital systems. However, Marple provides the mechanisms for applying this approach to analog systems such as the test bed, as well. The expert system was developed using Marple and Lucid Common Lisp running on a Sun Sparc-2 workstation. The Marple modeling environment has proved to be a useful tool for investigating the various aspects of model-based diagnostics. This report describes work completed to date and lessons learned while employing model-based diagnostics using constraint suspension within an analog system.
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.
The SLDPF is responsible for the capture, quality monitoring processing, accounting, and shipment of Spacelab and/or Attached Shuttle Payloads (ASP) telemetry data to various user facilities. Expert systems will aid in the performance of the quality assurance and data accounting functions of the two SLDPF functional elements: the Spacelab Input Processing System (SIPS) and the Spacelab Output Processing System (SOPS). Prototypes were developed for each as independent efforts. The SIPS Knowledge System Prototype (KSP) used the commercial shell OPS5+ on an IBM PC/AT; the SOPS Expert System Prototype used the expert system shell CLIPS implemented on a Macintosh personal computer. Both prototypes emulate the duties of the respective QA/DA analysts based upon analyst input and predetermined mission criteria parameters, and recommended instructions and decisions governing the reprocessing, release, or holding for further analysis of data. These prototypes demonstrated feasibility and high potential for operational systems. Increase in productivity, decrease of tedium, consistency, concise historial records, and a training tool for new analyses were the principal advantages. An operational configuration, taking advantage of the SLDPF network capabilities, is under development with the expert systems being installed on SUN workstations. This new configuration in conjunction with the potential of the expert systems will enhance the efficiency, in both time and quality, of the SLDPF's release of Spacelab/AST data products.