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NASA NTRS1989

Diagnostic tolerance for missing sensor data

For practical automated diagnostic systems to continue functioning after failure, they must not only be able to diagnose sensor failures but also be able to tolerate the absence of data from the faulty sensors. It is shown that conventional (associational) diagnostic methods will have combinatoric problems when trying to isolate faulty sensors, even if they adequately diagnose other components. Moreover, attempts to extend the operation of diagnostic capability past sensor failure will necessarily compound those difficulties. Model-based reasoning offers a structured alternative that has no special problems diagnosing faulty sensors and can operate gracefully when sensor data is missing.

Scarl, Ethan A.↗

NASA NTRS1990

Engine Data Interpretation System (EDIS)

A prototype of an expert system was developed which applies qualitative or model-based reasoning to the task of post-test analysis and diagnosis of data resulting from a rocket engine firing. A combined component-based and process theory approach is adopted as the basis for system modeling. Such an approach provides a framework for explaining both normal and deviant system behavior in terms of individual component functionality. The diagnosis function is applied to digitized sensor time-histories generated during engine firings. The generic system is applicable to any liquid rocket engine but was adapted specifically in this work to the Space Shuttle Main Engine (SSME). The system is applied to idealized data resulting from turbomachinery malfunction in the SSME.

Cost, Thomas L.↗

NASA NTRS1990

Demonstrating artificial intelligence for space systems - Integration and project management issues

As part of its Systems Autonomy Demonstration Project (SADP), NASA has recently demonstrated the Thermal Expert System (TEXSYS). Advanced real-time expert system and human interface technology was successfully developed and integrated with conventional controllers of prototype space hardware to provide intelligent fault detection, isolation, and recovery capability. Many specialized skills were required, and responsibility for the various phases of the project therefore spanned multiple NASA centers, internal departments and contractor organizations. The test environment required communication among many types of hardware and software as well as between many people. The integration, testing, and configuration management tools and methodologies which were applied to the TEXSYS project to assure its safe and successful completion are detailed. The project demonstrated that artificial intelligence technology, including model-based reasoning, is capable of the monitoring and control of a large, complex system in real time.

Hack, Edmund C.↗

NASA NTRS1991

ECLSS predictive monitoring

On Space Station Freedom (SSF), design iterations have made clear the need to keep the sensor complement small. Along with the unprecendented duration of the mission, it is imperative that decisions regarding placement of sensors be carefully examined and justified during the design phase. In the ECLSS Predictive Monitoring task, we are developing AI-based software to enable design engineers to evaluate alternate sensor configurations. Based on techniques from model-based reasoning and information theory, the software tool makes explicit the quantitative tradeoffs among competing sensor placements, and helps designers explore and justify placement decisions. This work is being applied to the Environmental Control and Life Support System (ECLSS) testbed at MSFC to assist design personnel in placing sensors for test purposes to evaluate baseline configurations and ultimately to select advanced life support system technologies for evolutionary SSF.

Doyle, Richard J.↗

NASA NTRS1991

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.↗

NASA NTRS1992

An expert system for monitoring dynamic aspects of RCS

An expert system (ARC) based on the MIMIC(2) framework is being developed for assistance in monitoring the Space Shuttle reaction control system (RCS) and in training RCS mission controllers. ARC has four components: a hypothesis generator, a model-constructor, a model-simulator and a matcher. The hypothesis generator examines current readings of sensors to generate a set of hypotheses. The model constructor generates models from these hypotheses. The simulator predicts future behaviors of the system. The matcher validates hypotheses by testing the predicted states against real data. The hypothesis generator postulates faults using model-based reasoning (MBR), learned rules from real data, and expert-defined rules. The simulator uses a qualitative QSIM model of the RCS which can handle both nominal modes as well as a variety of fault models. This allows the simulator to simulate the system even when one or more components have degraded. Finally, the matcher is responsible for comparing readings against predictions and calculating confidence in a particular hypothesis.

Kulkarni, Deepak↗

NASA NTRS1992

A vision architecture for the extravehicular activity retriever

The Extravehicular Activity Retriever (EVAR) is a robotic device currently being developed by the Automation and Robotics Division at the NASA Johnson Space Center to support activities in the neighborhood of the Space Shuttle or Space Station Freedom. As the name implies, the Retriever's primary function will be to provide the capability to retrieve tools, equipment or other objects which have become detached from the spacecraft, but it will also be able to rescue a crew member who may have become inadvertently de-tethered. Later goals will include cooperative operations between a crew member and the Retriever such as fetching a tool that is required for servicing or maintenance operations. This report documents a preliminary design for a Vision System Planner (VSP) for the EVAR that is capable of achieving visual objectives provided to it by a high level task planner. Typical commands which the task planner might issue to the VSP relate to object recognition, object location determination, and obstacle detection. Upon receiving a command from the task planner, the VSP then plans a sequence of actions to achieve the specified objective using a model-based reasoning approach. This sequence may involve choosing an appropriate sensor, selecting an algorithm to process the data, reorienting the sensor, adjusting the effective resolution of the image using lens zooming capability, and/or requesting the task planner to reposition the EVAR to obtain a different view of the object. An initial version of the Vision System Planner which realizes the above capabilities using simulated images has been implemented and tested. The remaining sections describe the architecture and capabilities of the VSP and its relationship to the high level task planner. In addition, typical plans that are generated to achieve visual goals for various scenarios will be discussed. Specific topics to be addressed will include object search strategies, repositioning of the EVAR to improve the quality of information obtained from the sensors, complementary usage of the sensors and redundant capabilities.

Magee, Michael↗

NASA NTRS1990

Real-time data system: Incorporating new technology in mission critical environments

If the Space Station Freedom is to remain viable over its 30-year life span, it must be able to incorporate new information systems technologies. These technologies are necessary to enhance mission effectiveness and to enable new NASA missions, such as supporting the Lunar-Mars Initiative. Hi-definition television (HDTV), neural nets, model-based reasoning, advanced languages, CPU designs, and computer networking standards are areas which have been forecasted to make major strides in the next 30 years. A major challenge to NASA is to bring these technologies online without compromising mission safety. In past programs, NASA managers have been understandably reluctant to rely on new technologies for mission critical activities until they are proven in noncritical areas. NASA must develop strategies to allow inflight confidence building and migration of technologies into the trusted tool base. NASA has successfully met this challenge and developed a winning strategy in the Space Shuttle Mission Control Center. This facility, which is clearly among NASA's most critical, is based on 1970's mainframe architecture. Changes to the mainframe are very expensive due to the extensive testing required to prove that changes do not have unanticipated impact on critical processes. Systematic improvement efforts in this facility have been delayed due to this 'risk to change.' In the real-time data system (RTDS) we have introduced a network of engineering computer workstations which run in parallel to the mainframe system. These workstations are located next to flight controller operating positions in mission control and, in some cases, the display units are mounted in the traditional mainframe consoles. This system incorporates several major improvements over the mainframe consoles including automated fault detection by real-time expert systems and color graphic animated schematics of subsystems driven by real-time telemetry. The workstations have the capability of recording telemetry data and providing 'instant replay' for flight controllers. RTDS also provides unique graphics animated by real-time telemetry such as workstation emulation of the shuttle's flight instruments and displays of the remote manipulator system (RMS) position. These systems have been used successfully as prime operational tools since STS-26 and have supported seven shuttle missions.

Muratore, John F.↗

NASA NTRS1991

System monitoring and diagnosis with qualitative models

A substantial foundation of tools for model-based reasoning with incomplete knowledge was developed: QSIM (a qualitative simulation program) and its extensions for qualitative simulation; Q2, Q3 and their successors for quantitative reasoning on a qualitative framework; and the CC (component-connection) and QPC (Qualitative Process Theory) model compilers for building QSIM QDE (qualitative differential equation) models starting from different ontological assumptions. Other model-compilers for QDE's, e.g., using bond graphs or compartmental models, have been developed elsewhere. These model-building tools will support automatic construction of qualitative models from physical specifications, and further research into selection of appropriate modeling viewpoints. For monitoring and diagnosis, plausible hypotheses are unified against observations to strengthen or refute the predicted behaviors. In MIMIC (Model Integration via Mesh Interpolation Coefficients), multiple hypothesized models of the system are tracked in parallel in order to reduce the 'missing model' problem. Each model begins as a qualitative model, and is unified with a priori quantitative knowledge and with the stream of incoming observational data. When the model/data unification yields a contradiction, the model is refuted. When there is no contradiction, the predictions of the model are progressively strengthened, for use in procedure planning and differential diagnosis. Only under a qualitative level of description can a finite set of models guarantee the complete coverage necessary for this performance. The results of this research are presented in several publications. Abstracts of these published papers are presented along with abtracts of papers representing work that was synergistic with the NASA grant but funded otherwise. These 28 papers include but are not limited to: 'Combined qualitative and numerical simulation with Q3'; 'Comparative analysis and qualitative integral representations'; 'Model-based monitoring of dynamic systems'; 'Numerical behavior envelopes for qualitative models'; 'Higher-order derivative constraints in qualitative simulation'; and 'Non-intersection of trajectories in qualitative phase space: a global constraint for qualitative simulation.'

Kuipers, Benjamin↗

NASA NTRS1992

Thermal Expert System (TEXSYS): Systems automony demonstration project, volume 1. Overview

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS test bed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

Glass, B. J.↗

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 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 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↗