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Results for “model-based reasoner”

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 73 records · Page 4
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NASA NTRS1992

An evidential reasoning extension to quantitative model-based failure diagnosis

The detection and diagnosis of failures in physical systems characterized by continuous-time operation are studied. A quantitative diagnostic methodology has been developed that utilizes the mathematical model of the physical system. On the basis of the latter, diagnostic models are derived each of which comprises a set of orthogonal parity equations. To improve the robustness of the algorithm, several models may be used in parallel, providing potentially incomplete and/or conflicting inferences. Dempster's rule of combination is used to integrate evidence from the different models. The basic probability measures are assigned utilizing quantitative information extracted from the mathematical model and from online computation performed therewith.

Gertler, Janos J.↗
NASA NTRS1991

Reasoning about real-time systems with temporal interval logic constraints on multi-state automata

Models of real-time systems using a single paradigm often turn out to be inadequate, whether the paradigm is based on states, rules, event sequences, or logic. A model-based approach to reasoning about real-time systems is presented in which a temporal interval logic called TIL is employed to define constraints on a new type of high level automata. The combination, called hierarchical multi-state (HMS) machines, can be used to model formally a real-time system, a dynamic set of requirements, the environment, heuristic knowledge about planning-related problem solving, and the computational states of the reasoning mechanism. In this framework, mathematical techniques were developed for: (1) proving the correctness of a representation; (2) planning of concurrent tasks to achieve goals; and (3) scheduling of plans to satisfy complex temporal constraints. HMS machines allow reasoning about a real-time system from a model of how truth arises instead of merely depending of what is true in a system.

Gabrielian, Armen↗
NASA NTRS1996

Immobile Robots: AI in the New Millennium

A new generation of sensor rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. These include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biosphere-like life support systems, and reconfigurable traffic systems, to highlight but a few. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions. To accomplish this, immobots will exploit a vast nervous system of sensors to model themselves and their environment on a grand scale. They will use these models to dramatically reconfigure themselves in order to survive decades of autonomous operations. Achieving these large scale modeling and configuration tasks will require a tight coupling between the higher level coordination function provided by symbolic reasoning, and the lower level autonomic processes of adaptive estimation and control. To be economically viable they will need to be programmable purely through high level compositional models. Self modeling and self configuration, coordinating autonomic functions through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous systems architecture that is taking us into the New Millennium.

Williams, Brian C.↗
NASA NTRS1991

TEXSYS - A large scale demonstration of model-based real-time control of a Space Station subsystem

A hybrid approach to qualitative and temporal reasoning, using a device-oriented model-based representation in conjunction with both consistency-based and classification diagnosis methods, can be effectively used to monitor and control a complex electromechanical system. An example of this approach is given in some recent tests of the Thermal Expert System (TEXSYS) in control of the Boeing Aerospace Thermal Bus System (BATBS), a prototype two-phase Space Station Freedom thermal bus. The constraints of realtime performance and changing target hardware led to significant changes in the initial approach, including a reduction in the use of deep structural reasoning and the addition of temporal reasoning capabilities. TEXSYS test results show the successful completion of both nominal control and fault recovery actions with the BATBS.

Glass, B. J.↗
NASA NTRS1986

Closed-loop, pilot/vehicle analysis of the approach and landing task

In the case of approach and landing, it is universally accepted that the pilot uses more than one vehicle response, or output, to close his control loops. Therefore, to model this task, a multi-loop analysis technique is required. The analysis problem has been in obtaining reasonable analytic estimates of the describing functions representing the pilot's loop compensation. Once these pilot describing functions are obtained, appropriate performance and workload metrics must then be developed for the landing task. The optimal control approach provides a powerful technique for obtaining the necessary describing functions, once the appropriate task objective is defined in terms of a quadratic objective function. An approach is presented through the use of a simple, reasonable objective function and model-based metrics to evaluate loop performance and pilot workload. The results of an analysis of the LAHOS (Landing and Approach of Higher Order Systems) study performed by R.E. Smith is also presented.

Anderson, M. R.↗
NASA NTRS1987

Diagnosis and sensor validation through knowledge of structure and function

The liquid oxygen expert system 'LES' is proposed as the first capable of diagnostic reasoning from sensor data, using model-based knowledge of structure and function to find the expected state of all system objects, including sensors. The approach is generally algorithmic rather than heuristic, and represents uncertainties as sets of possibilities. Functional relationships are inverted to determine hypothetical values for potentially faulty objects, and may include conditional functions not normally considered to have inverses.

Scarl, Ethan A.↗
NASA NTRS1987

Temporal and contextual knowledge in model-based expert systems

A basic paradigm that allows representation of physical systems with a focus on context and time is presented. Paragon provides the capability to quickly capture an expert's knowledge in a cognitively resonant manner. From that description, Paragon creates a simulation model in LISP, which when executed, verifies that the domain expert did not make any mistakes. The Achille's heel of rule-based systems has been the lack of a systematic methodology for testing, and Paragon's developers are certain that the model-based approach overcomes that problem. The reason this testing is now possible is that software, which is very difficult to test, has in essence been transformed into hardware.

Toth-Fejel, Tihamer↗
NASA NTRS1995

Comparing Simultaneous Stratospheric Aerosol and Ozone Lidar Measurements with SAGE 2 Data after the Mount Pinatubo Eruption

Stratospheric aerosol and ozone profiles obtained simultaneously from the lidar station at the University of L'Aquila (42.35 deg N, 13.33 deg E, 683 m above sea level) during the first 6 months following the eruption of Mount Pinatubo are compared with corresponding nearby Stratospheric Aerosol and Gas Experiment (SAGE) 2 profiles. The agreement between the two data sets is found to be reasonably good. The temporal change of aerosol profiles obtained by both techniques showed the intrusion and growth of Pinatubo aerosols. In addition, ozone concentration profiles derived from an empirical time-series model based on SAGE 2 ozone data obtained before the Pinatubo eruption are compared with measured profiles. Good agreement is shown in the 1991 profiles, but ozone concentrations measured in January 1992 were reduced relative to time-series model estimates. Possible reasons for the differences between measured and model-based ozone profiles are discussed.

Yue, G. K.↗
NASA NTRS1991

An SSME high pressure oxidizer turbopump diagnostic system using G2(TM) real-time expert system

An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2(TM) real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for Space Shuttle Main Engine. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach was adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.

Guo, Ten-Huei↗
NASA NTRS1991

An SSME High Pressure Oxidizer Turbopump diagnostic system using G2 real-time expert system

An expert system which diagnoses various seal leakage faults in the High Pressure Oxidizer Turbopump of the SSME was developed using G2 real-time expert system. Three major functions of the software were implemented: model-based data generation, real-time expert system reasoning, and real-time input/output communication. This system is proposed as one module of a complete diagnostic system for the SSME. Diagnosis of a fault is defined as the determination of its type, severity, and likelihood. Since fault diagnosis is often accomplished through the use of heuristic human knowledge, an expert system based approach has been adopted as a paradigm to develop this diagnostic system. To implement this approach, a software shell which can be easily programmed to emulate the human decision process, the G2 Real-Time Expert System, was selected. Lessons learned from this implementation are discussed.

Guo, Ten-Huei↗
NASA NTRS1993

Extending model-based diagnosis for analog thermodynamical devices

The increasing complexity of process control applications have posed difficult problems in fault detection, isolation, and recovery. Deep knowledge-based approaches, such as model-based diagnosis, have offered some promise in addressing these problems. However, the difficulties of adapting these techniques to situations involving numerical reasoning and noise have limited the applicability of these techniques. This paper describes an extension of classical model-based diagnosis techniques to deal with sparse data, noise, and complex noninvertible numerical models. These diagnosis techniques are being applied to the External Active Thermal Control System for Space Station Freedom.

Rouquette, Nicolas↗
NASA NTRS1998

Model Based Autonomy for Robust Mars Operations

Space missions have historically relied upon a large ground staff, numbering in the hundreds for complex missions, to maintain routine operations. When an anomaly occurs, this small army of engineers attempts to identify and work around the problem. A piloted Mars mission, with its multiyear duration, cost pressures, half-hour communication delays and two-week blackouts cannot be closely controlled by a battalion of engineers on Earth. Flight crew involvement in routine system operations must also be minimized to maximize science return. It also may be unrealistic to require the crew have the expertise in each mission subsystem needed to diagnose a system failure and effect a timely repair, as engineers did for Apollo 13. Enter model-based autonomy, which allows complex systems to autonomously maintain operation despite failures or anomalous conditions, contributing to safe, robust, and minimally supervised operation of spacecraft, life support, In Situ Resource Utilization (ISRU) and power systems. Autonomous reasoning is central to the approach. A reasoning algorithm uses a logical or mathematical model of a system to infer how to operate the system, diagnose failures and generate appropriate behavior to repair or reconfigure the system in response. The 'plug and play' nature of the models enables low cost development of autonomy for multiple platforms. Declarative, reusable models capture relevant aspects of the behavior of simple devices (e.g. valves or thrusters). Reasoning algorithms combine device models to create a model of the system-wide interactions and behavior of a complex, unique artifact such as a spacecraft. Rather than requiring engineers to all possible interactions and failures at design time or perform analysis during the mission, the reasoning engine generates the appropriate response to the current situation, taking into account its system-wide knowledge, the current state, and even sensor failures or unexpected behavior.

Kurien, James A.↗
NASA NTRS1991

Getting expert systems off the ground: Lessons learned from integrating model-based diagnostics with prototype flight hardware

As an initial attempt to introduce expert system technology into an onboard environment, a model based diagnostic system using the TRW MARPLE software tool was integrated with prototype flight hardware and its corresponding control software. Because this experiment was designed primarily to test the effectiveness of the model based reasoning technique used, the expert system ran on a separate hardware platform, and interactions between the control software and the model based diagnostics were limited. While this project met its objective of showing that model based reasoning can effectively isolate failures in flight hardware, it also identified the need for an integrated development path for expert system and control software for onboard applications. In developing expert systems that are ready for flight, artificial intelligence techniques must be evaluated to determine whether they offer a real advantage onboard, identify which diagnostic functions should be performed by the expert systems and which are better left to the procedural software, and work closely with both the hardware and the software developers from the beginning of a project to produce a well designed and thoroughly integrated application.

Stephan, Amy↗
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 NTRS1995

Microwave and infrared simulations of an intense convective system and comparison with aircraft observations

A three-dimensional cloud model, radiative transfer model-based simulation system is tested and validated against the aircraft-based radiance observations of an intense convective system in southeastern Virginia on 29 June 1986 during the Cooperative Huntsville Meteorological Experiment. NASA's ER-2, a high-altitude research aircraft with a complement of radiometers operating at 11-micrometer infrared channel and 18-, 37-, 92-, and 183-GHz microwave channels provided data for this study. The cloud model successfully simulated the cloud system with regard to aircraft- and radar-observed cloud-top heights and diameters and with regard to radar-observed reflectivity structure. For the simulation time found to correspond best with the aircraft- and radar-observed structure, brightness temperatures T(sub b) are simulated and compared with observations for all the microwave frequencies along with the 11-micrometer infrared channel. Radiance calculations at the various frequencies correspond well with the aircraft observations in the areas of deep convection. The clustering of 37-147-GHz T(sub b) observations and the isolation of the 18-GHz values over the convective cores are well simulated by the model. The radiative transfer model, in general, is able to simulate the observations reasonably well from 18 GHz through 174 GHz within all convective areas of the cloud system. When the aircraft-observed 18- and 37-GHz, and 90- and 174-GHz T(sub b) are plotted against each other, the relationships have a gradual difference in the slope due to the differences in the ice particle size in the convective and more stratiform areas of the cloud. The model is able to capture these differences observed by the aircraft. Brightness temperature-rain rate relationships compare reasonably well with the aircraft observations in terms of the slope of the relationship. The model calculations are also extended to select high-frequency channels at 220, 340, and 400 GHz to simulate the Millimeter-wave Imaging Radiometer aircraft instrument to be flown in the near future. All three of these frequencies are able to discriminate the convective and anvil portions of the system, providing useful information similar to that from the frequencies below 183 GHz but with potentially enhanced spatial resolution from a satellite platform. In thin clouds, the dominant effect of water vapor is seen at 174, 340, and 400 GHz. In thick cloudy areas, the scattering effect is dominant at 90 and 220 GHz, while the overlaying water vapor can attenuate at 174, 340, and 400 GHz. All frequencies (90-400 GHz) show strong signatures in the core.

Prasad, N.↗
NASA NTRS2000

The 13th Technology of Deep Space One - Abstract

On October 24th, 1998, the Deep Space One (DS-1) spacecraft launched aboard a Delta II rocket as the first step towards the bold task of testing and validating 12 new technologies for future missions. This launch also represented yet another thrilling event; namely, the successful test and validation of a 13th heretofore undisclosed technology: model-based code-generation of the spacecraft's system-level fault-protection (FP) software from behavioral state diagrams and structural models.In this paper, we describe the process we used to leverage model-based code generation from state diagrams and structural specifications to better respond to the evolving requirements and scope of DS- I's system-level fault-protection design, development, test and operation. The evolution of the high-level design and the low-level changes in the flight software architecture and interfaces contributed to multiplying the number and frequency of fault-protection software releases thereby creating a multitude of software integration issues. To address the resulting software integration issues, we broadened the scope of code -eneration to other forms of model- based analysis techniques more traditionally associated with first-principle's reasoning about physical models. Additionally, we describe our in-flight launch and initial acquisition experience.

Rouquette, Nicolas↗
NASA NTRS2000

Verification and Validation of Autonomy Software at NASA

Autonomous software holds the promise of new operation possibilities, easier design and development, and lower operating costs. However, as those system close control loops and arbitrate resources on-board with specialized reasoning, the range of possible situations becomes very large and uncontrollable from the outside, making conventional scenario-based testing very inefficient. Analytic verification and validation (V&V) techniques, and model checking in particular, can provide significant help for designing autonomous systems in a more efficient and reliable manner, by providing a better coverage and allowing early error detection. This article discusses the general issue of V&V of autonomy software, with an emphasis towards model-based autonomy, model-checking techniques, and concrete experiments at NASA.

Pecheur, Charles↗
NASA NTRS2000

Verification and Validation of Autonomy Software at NASA

Autonomous software holds the promise of new operation possibilities, easier design and development and lower operating costs. However, as those system close control loops and arbitrate resources on board with specialized reasoning, the range of possible situations becomes very large and uncontrollable from the outside, making conventional scenario-based testing very inefficient. Analytic verification and validation (V&V) techniques, and model checking in particular, can provide significant help for designing autonomous systems in a more efficient and reliable manner, by providing a better coverage and allowing early error detection. This article discusses the general issue of V&V of autonomy software, with an emphasis towards model-based autonomy, model-checking techniques and concrete experiments at NASA.

Pecheur, Charles↗