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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 235 records · Page 13

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NASA NTRS1981

F-8 digital fly-by-wire aircraft analytic redundancy management flight test experience

The formulation and flight test results of an algorithm to detect and isolate the first failure of any one of twelve duplex control sensors being monitored are described. The technique uses like sensor output differences for fault detection while relying upon analytic redundancy relationships among unlike quantities to isolate the faulty sensor. The fault isolation logic utilizes the modified sequential probability ratio test, which explicitly accommodates the inevitable irreducible low frequency errors present in the analytic redundancy residuals. In addition, the algorithm uses sensor output selftest, which takes advantage of the duplex sensor structure by immediately removing a highly erratic sensor from control calculations and analytic redundancy relationships while awaiting a definitive fault isolation decision via analytic redundancy.

Deckert, J. C.↗

NASA NTRS1988

Development of a space-systems network testbed

This paper describes a communications network testbed which has been designed to allow the development of architectures and algorithms that meet the functional requirements of future NASA communication systems. The central hardware components of the Network Testbed are programmable circuit switching communication nodes which can be adapted by software or firmware changes to customize the testbed to particular architectures and algorithms. Fault detection, isolation, and reconfiguration has been implemented in the Network with a hybrid approach which utilizes features of both centralized and distributed techniques to provide efficient handling of faults within the Network.

Lala, Jaynarayan↗

NASA NTRS1990

NASA systems autonomy demonstration project: Advanced automation demonstration of Space Station Freedom thermal control system

The NASA Systems Autonomy Demonstration Project (SADP) was initiated in response to Congressional interest in Space station automation technology demonstration. The SADP is a joint cooperative effort between Ames Research Center (ARC) and Johnson Space Center (JSC) to demonstrate advanced automation technology feasibility using the Space Station Freedom Thermal Control System (TCS) test bed. A model-based expert system and its operator interface were developed by knowledge engineers, AI researchers, and human factors researchers at ARC working with the domain experts and system integration engineers at JSC. Its target application is a prototype heat acquisition and transport subsystem of a space station TCS. The demonstration is scheduled to be conducted at JSC in August, 1989. The demonstration will consist of a detailed test of the ability of the Thermal Expert System to conduct real time normal operations (start-up, set point changes, shut-down) and to conduct fault detection, isolation, and recovery (FDIR) on the test article. The FDIR will be conducted by injecting ten component level failures that will manifest themselves as seven different system level faults. Here, the SADP goals, are described as well as the Thermal Control Expert System that has been developed for demonstration.

Dominick, Jeffrey↗

NASA NTRS1983

Analytic redundance management for flight control sensors

The formulation and flight test results of an algorithm to detect and isolate the first failure of any one of 12 duplex control sensor signals (24 in all) being monitored are reviewed. The technique uses like signal differences for fault detection while relying upon analytic redundancy relationships among unlike quantities to isolate the faulty sensor. The fault isolation logic utilizes the modified sequential probability ratio test, which explicitly accommodates the inevitable, irreducible low frequency errors present in the analytic redundancy residuals. In addition, the algorithm used sensor output selftest, which takes advantage of the duplex sensor structure by immediately removing a hard failed sensor from control calculations and analytic redundancy relationships while awaiting a definitive fault isolation decision via analytic redundancy. This study represents a proof of concept demonstration of a methodology that is applied to duplex or higher flight control sensor configurations and, in addition, can monitor the health of one simplex signal per analytic redundancy relationship.

Deckert, J. C.↗

NASA NTRS1990

Results of the Systems Autonomy Demonstration Project

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for real-time control and fault detection, isolation and recovery (FDIR) of a prototype two-phase Space Station Freedom external control system (TCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as nominal 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 capabilities of TEXSYS were shown to be advantageous, particularly for detection of unforeseen faults and sensor failures.

Glass, B. J.↗

NASA NTRS2015

Software Testbed for Developing and Evaluating Integrated Autonomous Subsystems

To implement fault tolerant autonomy in future space systems, it will be necessary to integrate planning, adaptive control, and state estimation subsystems. However, integrating these subsystems is difficult, time-consuming, and error-prone. This paper describes Intelliface/ADAPT, a software testbed that helps researchers develop and test alternative strategies for integrating planning, execution, and diagnosis subsystems more quickly and easily. The testbed's architecture, graphical data displays, and implementations of the integrated subsystems support easy plug and play of alternate components to support research and development in fault-tolerant control of autonomous vehicles and operations support systems. Intelliface/ADAPT controls NASA's Advanced Diagnostics and Prognostics Testbed (ADAPT), which comprises batteries, electrical loads (fans, pumps, and lights), relays, circuit breakers, invertors, and sensors. During plan execution, an experimentor can inject faults into the ADAPT testbed by tripping circuit breakers, changing fan speed settings, and closing valves to restrict fluid flow. The diagnostic subsystem, based on NASA's Hybrid Diagnosis Engine (HyDE), detects and isolates these faults to determine the new state of the plant, ADAPT. Intelliface/ADAPT then updates its model of the ADAPT system's resources and determines whether the current plan can be executed using the reduced resources. If not, the planning subsystem generates a new plan that reschedules tasks, reconfigures ADAPT, and reassigns the use of ADAPT resources as needed to work around the fault. The resource model, planning domain model, and planning goals are expressed using NASA's Action Notation Modeling Language (ANML). Parts of the ANML model are generated automatically, and other parts are constructed by hand using the Planning Model Integrated Development Environment, a visual Eclipse-based IDE that accelerates ANML model development. Because native ANML planners are currently under development and not yet sufficiently capable, the ANML model is translated into the New Domain Definition Language (NDDL) and sent to NASA's EUROPA planning system for plan generation. The adaptive controller executes the new plan, using augmented, hierarchical finite state machines to select and sequence actions based on the state of the ADAPT system. Real-time sensor data, commands, and plans are displayed in information-dense arrays of timelines and graphs that zoom and scroll in unison. A dynamic schematic display uses color to show the real-time fault state and utilization of the system components and resources. An execution manager coordinates the activities of the other subsystems. The subsystems are integrated using the Internet Communications Engine (ICE). an object-oriented toolkit for building distributed applications.

Testbed↗

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

Thermal Expert System (TEXSYS): Systems autonomy demonstration project, volume 2. Results

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 testbed, 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 NTRSDate not supplied

IMUFDIR Tuning In Response To Structural Deformation Of The Orion Capsule

During system level Thermal Vacuum (TVAC) testing it was discovered that the pressure differential of the crew cabin in a vacuum would deflect the capsule slightly, causing significant misalignment of Orion’s three Inertial Measurement Units (IMUs). This phenomenon has been likened to a balloon expanding. When this misalignment was modeled in simulation as a function of atmospheric pressure, it was determined that the rate of deflection on reentry was significant enough to cause a mis-compare of the angular rates measured by the IMUs. This mis-comparison of the rates caused Orion’s Fault Detection Isolation and Recovery (FDIR) algorithm to falsely fail one of the IMUs. This manuscript will discuss how the IMUFDIR algorithm was detuned to be insensitive to these ballooning effects, while still meeting FDIR requirements to capture real faults in an IMU. All testing to validate this detuning was performed in a Six Degree of Freedom (6 DOF) Monte Carlo Simulation Environment.

Nicholas Rahaim↗

NASA NTRS1990

A Thermal Expert System (TEXSYS) development overview - AI-based control of a Space Station prototype thermal bus

A knowledge-based control system for real-time control and fault detection, isolation and recovery (FDIR) of a prototype two-phase Space Station Freedom external thermal control system (TCS) is discussed in this paper. The Thermal Expert System (TEXSYS) has been demonstrated in recent tests to be capable of both fault anticipation and detection and real-time control of the thermal bus. Performance requirements were achieved by using a symbolic control approach, layering model-based expert system software on a conventional numerical data acquisition and control system. The model-based capabilities of TEXSYS were shown to be advantageous during software development and testing. One representative example is given from on-line TCS tests of TEXSYS. The integration and testing of TEXSYS with a live TCS testbed provides some insight on the use of formal software design, development and documentation methodologies to qualify knowledge-based systems for on-line or flight applications.

Glass, B. J.↗

NASA NTRS1990

Attitude Determination and Control System (ADCS) and Maintenance and Diagnostic System (MDS): A maintenance and diagnostic system for Space Station Freedom

The Maintenance and Diagnostic System (MDS) that is being developed at Honeywell to enhance the Fault Detection Isolation and Recovery system (FDIR) for the Attitude Determination and Control System on Space Station Freedom is described. The MDS demonstrates ways that AI-based techniques can be used to improve the maintainability and safety of the Station by helping to resolve fault anomalies that cannot be fully determined by built-in-test, by providing predictive maintenance capabilities, and by providing expert maintenance assistance. The MDS will address the problems associated with reasoning about dynamic, continuous information versus only about static data, the concerns of porting software based on AI techniques to embedded targets, and the difficulties associated with real-time response. An initial prototype was built of the MDS. The prototype executes on Sun and IBM PS/2 hardware and is implemented in the Common Lisp; further work will evaluate its functionality and develop mechanisms to port the code to Ada.

Toms, David↗

NASA NTRS2009

A New Monte Carlo Filtering Method for the Diagnosis of Mission-Critical Failures

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the settings most likely to cause a mission-critical failure. This research benchmarks two treatment learning methods against standard optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. It is shown that these treatment learners are both faster than traditional methods and show demonstrably better results.

Gay, Gregory↗

NASA NTRS2010

Automatically Finding the Control Variables for Complex System Behavior

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the factors most likely to cause a mission-critical failure. The goal of this research is to comparatively assess treatment learning against state-of-the-art numerical optimization techniques. To achieve this, this paper benchmarks the TAR3 and TAR4.1 treatment learners against optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. The results clearly show that treatment learning is both faster and more accurate than traditional optimization methods.

Gay, Gregory↗

NASA NTRS2008

Sensor Selection and Data Validation for Reliable Integrated System Health Management

For new access to space systems with challenging mission requirements, effective implementation of integrated system health management (ISHM) must be available early in the program to support the design of systems that are safe, reliable, highly autonomous. Early ISHM availability is also needed to promote design for affordable operations; increased knowledge of functional health provided by ISHM supports construction of more efficient operations infrastructure. Lack of early ISHM inclusion in the system design process could result in retrofitting health management systems to augment and expand operational and safety requirements; thereby increasing program cost and risk due to increased instrumentation and computational complexity. Having the right sensors generating the required data to perform condition assessment, such as fault detection and isolation, with a high degree of confidence is critical to reliable operation of ISHM. Also, the data being generated by the sensors needs to be qualified to ensure that the assessments made by the ISHM is not based on faulty data. NASA Glenn Research Center has been developing technologies for sensor selection and data validation as part of the FDDR (Fault Detection, Diagnosis, and Response) element of the Upper Stage project of the Ares 1 launch vehicle development. This presentation will provide an overview of the GRC approach to sensor selection and data quality validation and will present recent results from applications that are representative of the complexity of propulsion systems for access to space vehicles. A brief overview of the sensor selection and data quality validation approaches is provided below. The NASA GRC developed Systematic Sensor Selection Strategy (S4) is a model-based procedure for systematically and quantitatively selecting an optimal sensor suite to provide overall health assessment of a host system. S4 can be logically partitioned into three major subdivisions: the knowledge base, the down-select iteration, and the final selection analysis. The knowledge base required for productive use of S4 consists of system design information and heritage experience together with a focus on components with health implications. The sensor suite down-selection is an iterative process for identifying a group of sensors that provide good fault detection and isolation for targeted fault scenarios. In the final selection analysis, a statistical evaluation algorithm provides the final robustness test for each down-selected sensor suite. NASA GRC has developed an approach to sensor data qualification that applies empirical relationships, threshold detection techniques, and Bayesian belief theory to a network of sensors related by physics (i.e., analytical redundancy) in order to identify the failure of a given sensor within the network. This data quality validation approach extends the state-of-the-art, from red-lines and reasonableness checks that flag a sensor after it fails, to include analytical redundancy-based methods that can identify a sensor in the process of failing. The focus of this effort is on understanding the proper application of analytical redundancy-based data qualification methods for onboard use in monitoring Upper Stage sensors.

Garg, Sanjay↗

NASA NTRS2010

Fault Management Technology Maturation for NASA's Constellation Program

This slide presentation reviews the maturation of fault management technology in preparation for the Constellation Program. There is a review of the Space Shuttle Main Engine (SSME) and a discussion of a couple of incidents with the shuttle main engine and tanking that indicated the necessity for predictive maintenance. Included is a review of the planned Ares I-X Ground Diagnostic Prototype (GDP) and further information about detection and isolation of faults using Testability Engineering and Maintenance System (TEAMS). Another system that being readied for use that detects anomalies, the Inductive Monitoring System (IMS). The IMS automatically learns how the system behaves and alerts operations it the current behavior is anomalous. The comparison of STS-83 and STS-107 (i.e., the Columbia accident) is shown as an example of the anomaly detection capabilities.

Waterman, Robert D.↗

NASA NTRS2005

A model-based executive for commanding robot teams

The paper presents a way to robustly command a system of systems as a single entity. Instead of modeling each component system in isolation and then manually crafting interaction protocols, this approach starts with a model of the collective population as a single system. By compiling the model into separate elements for each component system and utilizing a teamwork model for coordination, it circumvents the complexities of manually crafting robust interaction protocols. The resulting systems are both globally responsive by virtue of a team oriented interaction model and locally responsive by virtue of a distributed approach to model-based fault detection, isolation, and recovery.

domain compilations↗

NASA NTRS1992

Scheduling lessons learned from the Autonomous Power System

The Autonomous Power System (APS) project at NASA LeRC is designed to demonstrate the applications of integrated intelligent diagnosis, control, and scheduling techniques to space power distribution systems. The project consists of three elements: the Autonomous Power Expert System (APEX) for Fault Diagnosis, Isolation, and Recovery (FDIR); the Autonomous Intelligent Power Scheduler (AIPS) to efficiently assign activities start times and resources; and power hardware (Brassboard) to emulate a space-based power system. The AIPS scheduler was tested within the APS system. This scheduler is able to efficiently assign available power to the requesting activities and share this information with other software agents within the APS system in order to implement the generated schedule. The AIPS scheduler is also able to cooperatively recover from fault situations by rescheduling the affected loads on the Brassboard in conjunction with the APEX FDIR system. AIPS served as a learning tool and an initial scheduling testbed for the integration of FDIR and automated scheduling systems. Many lessons were learned from the AIPS scheduler and are now being integrated into a new scheduler called SCRAP (Scheduler for Continuous Resource Allocation and Planning). This paper will service three purposes: an overview of the AIPS implementation, lessons learned from the AIPS scheduler, and a brief section on how these lessons are being applied to the new SCRAP scheduler.

Ringer, Mark J.↗

NASA NTRSDate not supplied

Artemis II Inertial Sensor Failure & Abort Condition Study

Given Artemis II as the first crewed lunar mission in decades, significant effort is directed towards understanding Space Launch System (SLS) response to failures of the three inertial sensors: two Ring-Laser Gyro Assemblies (RGAs) and the Redundant Inertial Navigation Unit (RINU). Parametric studies identify Loss of Mission (LOM) or Loss of Crew (LOC) sensitive mission phases. Fault Detection & Isolation (FDI) algorithms’ thresholds’ performance is assessed with respect to detecting LOM or LOC causing sensor failures. Monte Carlo studies are performed targeting specific trajectory phases for insight into re-optimizing FDI thresholds and improving knowledge of SLS sensor failure response statistics.

SLS↗