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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 37 records · Page 2

Real-time diagnostics of the reusable rocket engine using on-line system identification

A model-based failure diagnosis system has been proposed for real-time diagnosis of SSME failures. Actuation, sensor, and system degradation failure modes are all considered by the proposed system. In the case of SSME actuation failures, it was shown that real-time identification can effectively be used for failure diagnosis purposes. It is a direct approach since it reduces the detection, isolation, and the estimation of the extent of the failures to the comparison of parameter values before and after the failure. As with any model-based failure detection system, the proposed approach requires a fault model that embodies the essential characteristics of the failure process. The proposed diagnosis approach has the added advantage that it can be used as part of an intelligent control system for failure accommodation purposes.

Guo, T.-H.↗

Knowledge-based Autonomous Test Engineer (KATE)

Mathematical models of system components have long been used to allow simulators to predict system behavior to various stimuli. Recent efforts to monitor, diagnose, and control real-time systems using component models have experienced similar success. NASA Kennedy is continuing the development of a tool for implementing real-time knowledge-based diagnostic and control systems called KATE (Knowledge based Autonomous Test Engineer). KATE is a model-based reasoning shell designed to provide autonomous control, monitoring, fault detection, and diagnostics for complex engineering systems by applying its reasoning techniques to an exchangeable quantitative model describing the structure and function of the various system components and their systemic behavior.

Parrish, Carrie L.↗

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.↗

An evidential approach to model-based satellite diagnosis

The paper presents an approach to the implementation of an automated satellite diagnosis capability which emphasizes techniques offering flexibility in the application of diagnostic strategies. The system utilizes a structural and behavioral model of the satellite; it uses a form of spreading activation to perform the diagnostic procedures on the model. A Dempster-Shafer evidential framework was implemented, making it possible to integrate evidence from the multiple sources necessary for building useful systems.

Bickmore, Timothy W.↗

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↗

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↗

The KATE shell: An implementation of model-based control, monitor and diagnosis

The conventional control and monitor software currently used by the Space Center for Space Shuttle processing has many limitations such as high maintenance costs, limited diagnostic capabilities and simulation support. These limitations have caused the development of a knowledge based (or model based) shell to generically control and monitor electro-mechanical systems. The knowledge base describes the system's structure and function and is used by a software shell to do real time constraints checking, low level control of components, diagnosis of detected faults, sensor validation, automatic generation of schematic diagrams and automatic recovery from failures. This approach is more versatile and more powerful than the conventional hard coded approach and offers many advantages over it, although, for systems which require high speed reaction times or aren't well understood, knowledge based control and monitor systems may not be appropriate.

Cornell, Matthew↗

An evaluation of a real-time fault diagnosis expert system for aircraft applications

A fault monitoring and diagnosis expert system called Faultfinder was conceived and developed to detect and diagnose in-flight failures in an aircraft. Faultfinder is an automated intelligent aid whose purpose is to assist the flight crew in fault monitoring, fault diagnosis, and recovery planning. The present implementation of this concept performs monitoring and diagnosis for a generic aircraft's propulsion and hydraulic subsystems. This implementation is capable of detecting and diagnosing failures of known and unknown (i.e., unforseeable) type in a real-time environment. Faultfinder uses both rule-based and model-based reasoning strategies which operate on causal, temporal, and qualitative information. A preliminary evaluation is made of the diagnostic concepts implemented in Faultfinder. The evaluation used actual aircraft accident and incident cases which were simulated to assess the effectiveness of Faultfinder in detecting and diagnosing failures. Results of this evaluation, together with the description of the current Faultfinder implementation, are presented.

Schutte, Paul C.↗

Global heating distributions for January 1979 calculated from GLA assimilated and simulated model-based datasets

This study surveys the large-scale distribution of heating for January 1979 obtained from five sources of information. Through intercomparison of these distributions, with emphasis on satellite-derived information, an investigation is conducted into the global distribution of atmospheric heating and the impact of observations on the diagnostic estimates of heating derived from assimilated datasets. The results indicate a substantial impact of satellite information on diagnostic estimates of heating in regions where there is a scarcity of conventional observations. The addition of satellite data provides information on the atmosphere's temperature and wind structure that is important for estimation of the global distribution of heating and energy exchange.

Schaack, Todd K.↗

TROUBLE 3: A fault diagnostic expert system for Space Station Freedom's power system

Designing Space Station Freedom has given NASA many opportunities to develop expert systems that automate onboard operations of space based systems. One such development, TROUBLE 3, an expert system that was designed to automate the fault diagnostics of Space Station Freedom's electric power system is described. TROUBLE 3's design is complicated by the fact that Space Station Freedom's power system is evolving and changing. TROUBLE 3 has to be made flexible enough to handle changes with minimal changes to the program. Three types of expert systems were studied: rule-based, set-covering, and model-based. A set-covering approach was selected for TROUBLE 3 because if offered the needed flexibility that was missing from the other approaches. With this flexibility, TROUBLE 3 is not limited to Space Station Freedom applications, it can easily be adapted to handle any diagnostic system.

Manner, David B.↗

ARCTRON: A Rapid Experimental Proving Ground for TPS Experiments and Arcjet Technology Development

Innovation in high-enthalpy facilities is fundamentally limited by the cost and risk of experimentation. New concepts for plasma control, diagnostics, facility components, and plasma-material interaction often require repeated iterations that are impractical to perform in production arcjets. As a result, promising ideas may remain unexplored or reach operational facilities only after significant development effort. ARCTRON is being developed as a rapid experimental proving ground where new ideas in plasma science, arcjet engineering, diagnostics, and material response can be conceived, tested, and quantitatively evaluated before transition to large-scale facilities. The platform combines radio-frequency (RF) and DC arc plasma generation, externally applied magnetic fields, configurable gas composition, reduced-pressure operation, laser heating, electrical biasing, and modular diagnostic access. These capabilities permit the plasma source, applied forcing, test article, and measurement configuration to be modified independently, allowing individual physical mechanisms to be isolated more readily than in a traditional test environment. One class of investigations addresses fundamental plasma-surface interaction physics. Conventional material tests often expose a specimen simultaneously to convective heating, reactive species, pressure, shear, radiation, and surface-current effects. The resulting material response may be measured accurately, while the contribution of each mechanism remains difficult to identify. ARCTRON is designed to vary these effects selectively. Plasma chemistry can be changed independently through configurable gas mixtures; magnetic fields and electrical biasing can modify charged-particle transport; laser heating can provide a non-plasma thermal input; and pressure, flow, and discharge mode can be varied over a broad operating space. This enables controlled tests of hypotheses involving surface catalycity, reactive-species transport, plasma-assisted oxidation, electromagnetic effects, shear, and the relative contributions of thermal and chemical loading. A second class of investigations enabled by this approach concerns the engineering of high-enthalpy facilities themselves. Arc-heated facilities are limited by electrode erosion, unstable arc attachment, localized heating, and damage to nozzles and other plasma-facing components. ARCTRON provides a lower-cost environment for testing concepts intended to mitigate these limitations. Candidate investigations include the use of applied magnetic fields to alter current paths and reduce plasma interaction with nozzle walls, ExB forcing to introduce controlled plasma rotation, magnetic or geometric approaches for distributing arc attachment, and alternative electrode or discharge configurations intended to reduce erosion and improve stability. Because the platform is reconfigurable, these concepts can be evaluated through repeated design--build--test cycles before they are considered for implementation in operational facilities. The platform also supports the development and validation of diagnostics that may be difficult to introduce initially into a large arcjet. Current and planned measurements include spatially resolved optical emission spectroscopy, electrostatic probes, fast imaging, pyrometry, calorimetry, laser-induced fluorescence, and absorption spectroscopy. These diagnostics are intended not merely to document a nominal operating condition, but to constrain the local plasma state and its relationship to component or material response. The modular facility geometry allows diagnostic concepts to be tested, calibrated, and compared under repeatable conditions before deployment in more demanding environments. ARCTRON is also supported by an integrated software suite. Automated control and data acquisition allow discharge parameters, gas composition, magnetic fields, diagnostic timing, and test configuration to be recorded as part of each experiment (STARDAC - Software for Testing, Analysis, Research Data, and Control). The Backend for Experiment Analysis, Storage, and Traceability (BEAST) is a database that provides the infrastructure needed to associate heterogeneous measurements with facility configuration, specimen identity, calibration state, geometry, and analysis provenance. This backend is particularly important for exploratory campaigns, in which many related configurations may be tested, and the value of an individual experiment depends on its connection to earlier and subsequent iterations. Complementary analysis capabilities, including computer-vision-based transient response measurements (arcjetCV), three-dimensional surface reconstruction (STARSCAN), and model-based Bayesian inference (SHIELD), and tomography data analysis (TOMATO, PuMA) can be incorporated when required by a specific hypothesis without becoming the focus of every campaign. The central objective of ARCTRON is therefore not to maximize heat flux or reproduce a complete flight environment. Its purpose is to reduce the cost and time required to ask consequential questions about plasma behavior, plasma-facing materials, diagnostics, and arcjet technology. By providing a controlled environment for rapid reconfiguration, mechanism isolation, quantitative measurement, and iterative engineering, ARCTRON can help mature concepts that would otherwise remain too speculative or too risky for evaluation in production facilities. The resulting knowledge can then guide the design of material models, focus test objectives in larger arcjets, reduce facility-development risk, and improve the physical basis of high-enthalpy ground testing. This work will present the ARCTRON architecture, operating modes, diagnostic suite, and digital experimental workflow. Initial experimental results from the first integrated operation of the facility will be presented, including flow characterization, power limitations, and deployment of the initial diagnostic suite. Ongoing development efforts aimed at catalycity characterization, magnetic plasma control, and advanced optical diagnostics will also be discussed, illustrating how the platform supports rapid iteration from concept to experiment.

experimental diagnostics↗

ARCTRON: A Rapid Experimental Proving Ground for TPS Experiments and Arcjet Technology Development

Innovation in high-enthalpy facilities is fundamentally limited by the cost and risk of experimentation. New concepts for plasma control, diagnostics, facility components, and plasma-material interaction often require repeated iterations that are impractical to perform in production arcjets. As a result, promising ideas may remain unexplored or reach operational facilities only after significant development effort. ARCTRON is being developed as a rapid experimental proving ground where new ideas in plasma science, arcjet engineering, diagnostics, and material response can be conceived, tested, and quantitatively evaluated before transition to large-scale facilities. The platform combines radio-frequency (RF) and DC arc plasma generation, externally applied magnetic fields, configurable gas composition, reduced-pressure operation, laser heating, electrical biasing, and modular diagnostic access. These capabilities permit the plasma source, applied forcing, test article, and measurement configuration to be modified independently, allowing individual physical mechanisms to be isolated more readily than in a traditional test environment. One class of investigations addresses fundamental plasma-surface interaction physics. Conventional material tests often expose a specimen simultaneously to convective heating, reactive species, pressure, shear, radiation, and surface-current effects. The resulting material response may be measured accurately, while the contribution of each mechanism remains difficult to identify. ARCTRON is designed to vary these effects selectively. Plasma chemistry can be changed independently through configurable gas mixtures; magnetic fields and electrical biasing can modify charged-particle transport; laser heating can provide a non-plasma thermal input; and pressure, flow, and discharge mode can be varied over a broad operating space. This enables controlled tests of hypotheses involving surface catalycity, reactive-species transport, plasma-assisted oxidation, electromagnetic effects, shear, and the relative contributions of thermal and chemical loading. A second class of investigations enabled by this approach concerns the engineering of high-enthalpy facilities themselves. Arc-heated facilities are limited by electrode erosion, unstable arc attachment, localized heating, and damage to nozzles and other plasma-facing components. ARCTRON provides a lower-cost environment for testing concepts intended to mitigate these limitations. Candidate investigations include the use of applied magnetic fields to alter current paths and reduce plasma interaction with nozzle walls, ExB forcing to introduce controlled plasma rotation, magnetic or geometric approaches for distributing arc attachment, and alternative electrode or discharge configurations intended to reduce erosion and improve stability. Because the platform is reconfigurable, these concepts can be evaluated through repeated design--build--test cycles before they are considered for implementation in operational facilities. The platform also supports the development and validation of diagnostics that may be difficult to introduce initially into a large arcjet. Current and planned measurements include spatially resolved optical emission spectroscopy, electrostatic probes, fast imaging, pyrometry, calorimetry, laser-induced fluorescence, and absorption spectroscopy. These diagnostics are intended not merely to document a nominal operating condition, but to constrain the local plasma state and its relationship to component or material response. The modular facility geometry allows diagnostic concepts to be tested, calibrated, and compared under repeatable conditions before deployment in more demanding environments. ARCTRON is also supported by an integrated software suite. Automated control and data acquisition allow discharge parameters, gas composition, magnetic fields, diagnostic timing, and test configuration to be recorded as part of each experiment (STARDAC - Software for Testing, Analysis, Research Data, and Control). The Backend for Experiment Analysis, Storage, and Traceability (BEAST) is a database that provides the infrastructure needed to associate heterogeneous measurements with facility configuration, specimen identity, calibration state, geometry, and analysis provenance. This backend is particularly important for exploratory campaigns, in which many related configurations may be tested, and the value of an individual experiment depends on its connection to earlier and subsequent iterations. Complementary analysis capabilities, including computer-vision-based transient response measurements (arcjetCV), three-dimensional surface reconstruction (STARSCAN), and model-based Bayesian inference (SHIELD), and tomography data analysis (TOMATO, PuMA) can be incorporated when required by a specific hypothesis without becoming the focus of every campaign. The central objective of ARCTRON is therefore not to maximize heat flux or reproduce a complete flight environment. Its purpose is to reduce the cost and time required to ask consequential questions about plasma behavior, plasma-facing materials, diagnostics, and arcjet technology. By providing a controlled environment for rapid reconfiguration, mechanism isolation, quantitative measurement, and iterative engineering, ARCTRON can help mature concepts that would otherwise remain too speculative or too risky for evaluation in production facilities. The resulting knowledge can then guide the design of material models, focus test objectives in larger arcjets, reduce facility-development risk, and improve the physical basis of high-enthalpy ground testing. This work will present the ARCTRON architecture, operating modes, diagnostic suite, and digital experimental workflow. Initial experimental results from the first integrated operation of the facility will be presented, including flow characterization, power limitations, and deployment of the initial diagnostic suite. Ongoing development efforts aimed at catalycity characterization, magnetic plasma control, and advanced optical diagnostics will also be discussed, illustrating how the platform supports rapid iteration from concept to experiment.

experimental diagnostics↗

Diagnosing faults in autonomous robot plan execution

A major requirement for an autonomous robot is the capability to diagnose faults during plan execution in an uncertain environment. Many diagnostic researches concentrate only on hardware failures within an autonomous robot. Taking a different approach, the implementation of a Telerobot Diagnostic System that addresses, in addition to the hardware failures, failures caused by unexpected event changes in the environment or failures due to plan errors, is described. One feature of the system is the utilization of task-plan knowledge and context information to deduce fault symptoms. This forward deduction provides valuable information on past activities and the current expectations of a robotic event, both of which can guide the plan-execution inference process. The inference process adopts a model-based technique to recreate the plan-execution process and to confirm fault-source hypotheses. This technique allows the system to diagnose multiple faults due to either unexpected plan failures or hardware errors. This research initiates a major effort to investigate relationships between hardware faults and plan errors, relationships which were not addressed in the past. The results of this research will provide a clear understanding of how to generate a better task planner for an autonomous robot and how to recover the robot from faults in a critical environment.

Lam, Raymond K.↗

Diagnosing faults in autonomous robot plan execution

A major requirement for an autonomous robot is the capability to diagnose faults during plan execution in an uncertain environment. Many diagnostic researches concentrate only on hardware failures within an autonomous robot. Taking a different approach, the implementation of a Telerobot Diagnostic System that addresses, in addition to the hardware failures, failures caused by unexpected event changes in the environment or failures due to plan errors, is described. One feature of the system is the utilization of task-plan knowledge and context information to deduce fault symptoms. This forward deduction provides valuable information on past activities and the current expectations of a robotic event, both of which can guide the plan-execution inference process. The inference process adopts a model-based technique to recreate the plan-execution process and to confirm fault-source hypotheses. This technique allows the system to diagnose multiple faults due to either unexpected plan failures or hardware errors. This research initiates a major effort to investigate relationships between hardware faults and plan errors, relationships which were not addressed in the past. The results of this research will provide a clear understanding of how to generate a better task planner for an autonomous robot and how to recover the robot from faults in a critical environment.

Lam, Raymond K.↗

Real-time diagnostics for a reusable rocket engine

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2.

Guo, T. H.↗

Diagnosis by integrating model-based reasoning with knowledge-based reasoning

Our research investigates how observations can be categorized by integrating a qualitative physical model with experiential knowledge. Our domain is diagnosis of pathologic gait in humans, in which the observations are the gait motions, muscle activity during gait, and physical exam data, and the diagnostic hypotheses are the potential muscle weaknesses, muscle mistimings, and joint restrictions. Patients with underlying neurological disorders typically have several malfunctions. Among the problems that need to be faced are: the ambiguity of the observations, the ambiguity of the qualitative physical model, correspondence of the observations and hypotheses to the qualitative physical model, the inherent uncertainty of experiential knowledge, and the combinatorics involved in forming composite hypotheses. Our system divides the work so that the knowledge-based reasoning suggests which hypotheses appear more likely than others, the qualitative physical model is used to determine which hypotheses explain which observations, and another process combines these functionalities to construct a composite hypothesis based on explanatory power and plausibility. We speculate that the reasoning architecture of our system is generally applicable to complex domains in which a less-than-perfect physical model and less-than-perfect experiential knowledge need to be combined to perform diagnosis.

Bylander, Tom↗

Knowledge-based process control and diagnostics for orbital cryogen transfer

AFDex is a rule based system designed to provide intelligent process control, diagnosis, and error recovery for a Shuttle based cryogenic experiment, SHOOT (Superfluid Helium On-Orbit Transfer). This paper describes the AFDex system in the context of traditional associative, model-based, and qualitative systems and discusses the implications of this first expert system in space.

Raymond, Eric A.↗

CONFIG - Adapting qualitative modeling and discrete event simulation for design of fault management systems

CONFIG is a modeling and simulation tool prototype for analyzing the normal and faulty qualitative behaviors of engineered systems. Qualitative modeling and discrete-event simulation have been adapted and integrated, to support early development, during system design, of software and procedures for management of failures, especially in diagnostic expert systems. Qualitative component models are defined in terms of normal and faulty modes and processes, which are defined by invocation statements and effect statements with time delays. System models are constructed graphically by using instances of components and relations from object-oriented hierarchical model libraries. Extension and reuse of CONFIG models and analysis capabilities in hybrid rule- and model-based expert fault-management support systems are discussed.

Malin, Jane T.↗