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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Diagnostic for Verifying the Thrust Vector Requirement of the AEPS Hall-Effect Thruster and Comparison to the NEXT-C Thrust Vector Diagnostic

A diagnostic has been designed and fabricated to verify the thrust vector requirement for the Advanced Electric Propulsion System (AEPS) Hall Effect Rocket with Magnetic Shielding (HERMeS) thruster. This diagnostic will be used to verify that the propulsion system thrust vector offset from the mounting surface normal vector does not exceed 1.5 degrees over the entire throttling range and over the course of 23,000 hours of thruster testing. The diagnostic will also not violate the thruster's required voltage standoff capability in the presence of carbon backsputter by being minimally intrusive and not significantly adding to the facility backsputtered rate. Based on these requirements, an appropriate diagnostic design was determined to comprise of an array of 23 Faraday probes swept through the plume in an arc 1m from the thruster to map the beam current density. The beam current density centroid of the plume is assumed to track the thrust vector within an acceptable level of uncertainty. Additionally, a reference system, including optical alignment to the mounting surface normal vector and tilt sensors, was devised to periodically calibrate the probe position and motion throughout the long duration wear test campaign. Initial measurements of the thruster plume have been acquired to demonstrate the diagnostics functionality, verify procedures, and assess any necessary improvements prior to implementation of the diagnostic during the AEPS Engineering Development Unit (EDU) long duration wear test. To illustrate the merits of differing approaches to thrust vector determination for different classes of electric propulsion thrusters, NASA's Evolutionary Xenon Thruster-Commercial (NEXT-C) thrust vector diagnostic design details and recent data are also discussed (Appendix A).

Benavides, Gabriel F.↗
NASA NTRS2018

Diagnostic for Verifying the Thrust Vector Requirement of the AEPS Hall-Effect Thruster and Comparison to the NEXT-C Thrust Vector Diagnostic

A diagnostic has been designed and fabricated to verify the thrust vector requirement for the Advanced Electric Propulsion System (AEPS) Hall Effect Rocket with Magnetic Shielding (HERMeS) thruster. This diagnostic will be used to verify that the propulsion system thrust vector offset from the mounting surface normal vector does not exceed 1.5 degrees over the entire throttling range and over the course of 23,000 hours of thruster testing. The diagnostic will also not violate the thruster's required voltage standoff capability in the presence of carbon backsputter by being minimally intrusive and not significantly adding to the facility backsputtered rate. Based on these requirements, an appropriate diagnostic design was determined to comprise of an array of 23 Faraday probes swept through the plume in an arc 1m from the thruster to map the beam current density. The beam current density centroid of the plume is assumed to track the thrust vector within an acceptable level of uncertainty. Additionally, a reference system, including optical alignment to the mounting surface normal vector and tilt sensors, was devised to periodically calibrate the probe position and motion throughout the long duration wear test campaign. Initial measurements of the thruster plume have been acquired to demonstrate the diagnostics functionality, verify procedures, and assess any necessary improvements prior to implementation of the diagnostic during the AEPS Engineering Development Unit (EDU) long duration wear test. To illustrate the merits of differing approaches to thrust vector determination for different classes of electric propulsion thrusters, NASA's Evolutionary Xenon Thruster-Commercial (NEXT-C) thrust vector diagnostic design details and recent data are also discussed (Appendix A).

Benavides, Gabriel F.↗
NASA NTRS2018

Diagnostic for Verifying the Thrust Vector Requirement of the AEPS Hall-Effect Thruster and Comparison to the NEXT-C Thrust Vector Diagnostic

A diagnostic has been designed and fabricated to verify the thrust vector requirement for the Advanced Electric Propulsion System (AEPS) Hall Effect Rocket with Magnetic Shielding (HERMeS) thruster. This diagnostic will be used to verify that the propulsion system thrust vector offset from the mounting surface normal vector does not exceed 1.5 degrees over the entire throttling range and over the course of 23,000 hours of thruster testing. The diagnostic will also not violate the thruster's required voltage standoff capability in the presence of carbon backsputter by being minimally intrusive and not significantly adding to the facility backsputtered rate. Based on these requirements, an appropriate diagnostic design was determined to comprise of an array of 23 Faraday probes swept through the plume in an arc 1m from the thruster to map the beam current density. The beam current density centroid of the plume is assumed to track the thrust vector within an acceptable level of uncertainty. Additionally, a reference system, including optical alignment to the mounting surface normal vector and tilt sensors, was devised to periodically calibrate the probe position and motion throughout the long duration wear test campaign. Initial measurements of the thruster plume have been acquired to demonstrate the diagnostics functionality, verify procedures, and assess any necessary improvements prior to implementation of the diagnostic during the AEPS Engineering Development Unit (EDU) long duration wear test. To illustrate the merits of differing approaches to thrust vector determination for different classes of electric propulsion thrusters, NASA's Evolutionary Xenon Thruster-Commercial (NEXT-C) thrust vector diagnostic design details and recent data are also discussed (Appendix A).

Advanced Electric Propulsion System (AEPS)↗
NASA NTRS2002

Comparison of the T(sub 1) and D(sub 1) Diagnostics: A New Definition for the Open-Shell D(sub 1) Diagnostic

It is shown that the T(sub 1) operator used in a previous study to define the open-shell D(sub 1) diagnostic is invalid, and leads to an arbitrary definition of the open-shell D(sub 1) diagnostic. A new definition is proposed that eliminates this ambiguity and approximately restores the mathematical relationship previously noted between the closed-shell D(sub 1) and T(sub 1) diagnostics. Statistical comparison of the T(sub 1) and D(sub 1) diagnostics shows a very high degree of correlation between them, although it is argued that both diagnostics used together can provide more information than either can separately.

Lee, Timothy J.↗
NASA NTRS1986

Studies and analyses of the Space Shuttle Main Engine: SSME failure data review, diagnostic survey and SSME diagnostic evaluation

The results of a review of the Space Shuttle Main Engine (SSME) failure data for the period 1980 through 1983 are presented. The data was collected, evaluated, and ranked according to procedures established during this study. A number of conclusions and recommendations are made based upon this failure data review. The results of a state-of-the-art diagnostic survey are also presented. This survey covered a broad range of diagnostic sensors and techniques and the findings were evaluated for application to the SSME. Finally, a discussion of the initial activities for the on-going SSME diagnostic evaluation is included.

Glover, R. C.↗
NASA NTRS1995

Investigation of a Diagnostic for Perturbation Theory: Comparison to the T(sub 1) Diagnostic of Coupled-Cluster Theory

A diagnostic for perturbation theory calculations, S(sub 2), is defined and numerical results are compared to the established T(sub 1) diagnostic from coupled-cluster theory. S(sub 2) is the lowest order non-zero contribution to a perturbation expansion of T(sub 1). S(sub 2) is a reasonable estimate of the importance of non-dynamical electron correlation, although not as reliable as T(sub 1). S(sub 2) values less than or equal to 0.012 suggest that low orders of perturbation theory should yield reasonable results; S(sub 2) values between 0.012-0.015 suggest that caution is required in interpreting results from low orders of perturbation theory; S(sub 2) values greater than or equal to 0.015 indicate that low orders of perturbation theory are not reliable for accurate results. Although not required mathematically, S(sub 2) is always less than T(sub 1) for the examples studied here.

Lee, Timothy 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 NTRS1994

Electron-Beam Diagnostic Methods for Hypersonic Flow Diagnostics

The purpose of this work was the evaluation of the use of electron-bean fluorescence for flow measurements during hypersonic flight. Both analytical and numerical models were developed in this investigation to evaluate quantitatively flow field imaging concepts based upon the electron beam fluorescence technique for use in flight research and wind tunnel applications. Specific models were developed for: (1) fluorescence excitation/emission for nitrogen, (2) rotational fluorescence spectrum for nitrogen, (3) single and multiple scattering of electrons in a variable density medium, (4) spatial and spectral distribution of fluorescence, (5) measurement of rotational temperature and density, (6) optical filter design for fluorescence imaging, and (7) temperature accuracy and signal acquisition time requirements. Application of these models to a typical hypersonic wind tunnel flow is presented. In particular, the capability of simulating the fluorescence resulting from electron impact ionization in a variable density nitrogen or air flow provides the capability to evaluate the design of imaging instruments for flow field mapping. The result of this analysis is a recommendation that quantitative measurements of hypersonic flow fields using electron-bean fluorescence is a tractable method with electron beam energies of 100 keV. With lower electron energies, electron scattering increases with significant beam divergence which makes quantitative imaging difficult. The potential application of the analytical and numerical models developed in this work is in the design of a flow field imaging instrument for use in hypersonic wind tunnels or onboard a flight research vehicle.

Source record↗
NASA NTRSDate not supplied

Novel Approach to Simulating Diagnostic Capabilities in Medical Resource Risk Assessment

Diagnostics represent a key subset of medical resources considered for helping mitigate and control spaceflight medical risk. Historically, when modeling the medical risk domain for spaceflight, diagnostic resources have been handled like any other medical resource used for treatment. That is to say, the resource being unavailable will result in the related condition having suboptimal treatment outcomes. However in the real world, should a diagnostic resource be unavailable, a more analogous effect would be that the condition might be misdiagnosed, and thus inappropriate treatment applied. This talk presents an alternative means of representing diagnostic resources within the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) that can simulate the effect both in risk, resource consumption, and competition for resources of a missing or depleted diagnostic. The scenario where a differential diagnosis may not be possible for abdominal conditions that use an ultrasound machine as a diagnostic tool is considered. Two approaches for modeling the effect of not having a diagnostic capability on the medical system are demonstrated. In the “simple” approach, it is assumed that any abdominal condition requiring the ultrasound machine for diagnosis is mis-diagnosed and treated as an appendicitis. This is represented by replacing the treatment for the affected conditions with the treatment for appendicitis, thereby changing the optimized medical set available for treatment for the remainder of the mission. In the “complex” approach, MEDPRAT treatment clusters are utilized to capture and represent overlapping treatment between the misdiagnosed condition and appendicitis. This elicits both the effect that inappropriate treatment is applied, resulting in wasted resource consumption, and that treatment which was truly needed for the misdiagnosed condition was not applied, which reduces the treatment effectiveness. In this talk, results comparing the simple and complex diagnostic capability approach to a baseline where the diagnostic is treated as a traditional resource is presented. Either approach provides an option for a more analogous representation of diagnostic resources and provides insight into how the modeled spaceflight medical resources and outcomes change when that diagnostic is unavailable.

C. M. Gasiewski↗
NASA NTRS1988

A design and implementation methodology for diagnostic systems

A methodology for design and implementation of diagnostic systems is presented. Also discussed are the advantages of embedding a diagnostic system in a host system environment. The methodology utilizes an architecture for diagnostic system development that is hierarchical and makes use of object-oriented representation techniques. Additionally, qualitative models are used to describe the host system components and their behavior. The methodology architecture includes a diagnostic engine that utilizes a combination of heuristic knowledge to control the sequence of diagnostic reasoning. The methodology provides an integrated approach to development of diagnostic system requirements that is more rigorous than standard systems engineering techniques. The advantages of using this methodology during various life cycle phases of the host systems (e.g., National Aerospace Plane (NASP)) include: the capability to analyze diagnostic instrumentation requirements during the host system design phase, a ready software architecture for implementation of diagnostics in the host system, and the opportunity to analyze instrumentation for failure coverage in safety critical host system operations.

Williams, Linda J. F.↗
NASA NTRS1992

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.↗
NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

Automating prelaunch diagnostics for launch vehicles offers three potential benefits. First, it potentially improves safety by detecting faults that might otherwise have been missed so that they can be corrected before launch. Second, it potentially reduces launch delays by more quickly diagnosing the cause of anomalies that occur during prelaunch processing. Reducing launch delays will be critical to the success of NASA's planned future missions that require in-orbit rendezvous. Third, it potentially reduces costs by reducing both launch delays and the number of people needed to monitor the prelaunch process. NASA is currently developing the Ares I launch vehicle to bring the Orion capsule and its crew of four astronauts to low-earth orbit on their way to the moon. Ares I-X will be the first unmanned test flight of Ares I. It is scheduled to launch on October 27, 2009. The Ares I-X Ground Diagnostic Prototype is a prototype ground diagnostic system that will provide anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage thrust vector control (TVC) and for the associated ground hydraulics while it is in the Vehicle Assembly Building (VAB) at John F. Kennedy Space Center (KSC) and on the launch pad. It will serve as a prototype for a future operational ground diagnostic system for Ares I. The prototype combines three existing diagnostic tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool that is commercially produced by Qualtech Systems, Inc. It uses a qualitative model of failure propagation to perform fault isolation and diagnostics. We adapted an existing TEAMS model of the TVC to use for diagnostics and developed a TEAMS model of the ground hydraulics. The second tool, Spacecraft Health Inference Engine (SHINE), is a rule-based expert system developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification. The prototype uses the outputs of SHINE as inputs to TEAMS. The third tool, the Inductive Monitoring System (IMS), is an anomaly detection tool developed at NASA Ames Research Center and is currently used to monitor the International Space Station Control Moment Gyroscopes. IMS automatically "learns" a model of historical nominal data in the form of a set of clusters and signals an alarm when new data fails to match this model. IMS offers the potential to detect faults that have not been modeled. The three tools have been integrated and deployed to Hangar AE at KSC where they interface with live data from the Ares I-X vehicle and from the ground hydraulics. The outputs of the tools are displayed on a console in Hangar AE, one of the locations from which the Ares I-X launch will be monitored. The full paper will describe how the prototype performed before the launch. It will include an analysis of the prototype's accuracy, including false-positive rates, false-negative rates, and receiver operating characteristics (ROC) curves. It will also include a description of the prototype's computational requirements, including CPU usage, main memory usage, and disk usage. If the prototype detects any faults during the prelaunch period then the paper will include a description of those faults. Similarly, if the prototype has any false alarms then the paper will describe them and will attempt to explain their causes.

Schwabacher, Mark↗
NASA NTRS1991

Development of an intelligent diagnostic system for reusable rocket engine control

A description of an intelligent diagnostic system for the Space Shuttle Main Engines (SSME) is presented. This system is suitable for incorporation in an intelligent controller which implements accommodating closed-loop control to extend engine life and maximize available performance. The diagnostic system architecture is a modular, hierarchical, blackboard system which is particularly well suited for real-time implementation of a system which must be repeatedly updated and extended. The diagnostic problem is formulated as a hierarchical classification problem in which the failure hypotheses are represented in terms of predefined data patterns. The diagnostic expert system incorporates techniques for priority-based diagnostics, the combination of analytical and heuristic knowledge for diagnosis, integration of different AI systems, and the implementation of hierarchical distributed systems. A prototype reusable rocket engine diagnostic system (ReREDS) has been implemented. The prototype user interface and diagnostic performance using SSME test data are described.

Anex, R. P.↗
NASA NTRS1995

Transmission diagnostic research at NASA Lewis Research Center

The NASA Lewis Research Center and the U.S. Army Research Laboratory are involved in a joint research program to advance the technology of aerospace transmissions. Within the last six years, a transmission diagnostics research team was formed to address current and future technology barriers in transmission diagnostics. The diagnostics team conducted a survey to determine critical needs of the diagnostics community. Survey results indicated that experimental verification of gear and bearing fault detection methods and damage magnitude assessment were considered the two most critical research areas of a highly reliable health and usage monitoring system. A plan was implemented by the diagnostics team to address these key research areas, by in-house research and university grants. A variety of transmission fault detection methods were applied to experimentally obtained fatigue data. Failure modes of the fatigue tests include a variety of gear pitting failures, tooth wear, tooth fracture, and bearing spalling failures. Accomplishments to date include verification of several specific gear diagnostic methods, verification of a new pattern recognition method to determine failure, and development of a new method to model gear tooth damage. This paper presents the results of these accomplishments in transmission diagnostics research at NASA Lewis Research Center.

Zakrajsek, J. J.↗
NASA NTRS2002

A Scalable, Out-of-Band Diagnostics Architecture for International Space Station Systems Support

The computational infrastructure of the International Space Station (ISS) is a dynamic system that supports multiple vehicle subsystems such as Caution and Warning, Electrical Power Systems and Command and Data Handling (C&DH), as well as scientific payloads of varying size and complexity. The dynamic nature of the ISS configuration coupled with the increased demand for payload support places a significant burden on the inherently resource constrained computational infrastructure of the ISS. Onboard system diagnostics applications are hosted on computers that are elements of the avionics network while ground-based diagnostic applications receive only a subset of available telemetry, down-linked via S-band communications. In this paper we propose a scalable, out-of-band diagnostics architecture for ISS systems support that uses a read-only connection for C&DH data acquisition, which provides a lower cost of deployment and maintenance (versus a higher criticality readwrite connection). The diagnostics processing burden is off-loaded from the avionics network to elements of the on-board LAN that have a lower overall cost of operation and increased computational capacity. A superset of diagnostic data, richer in content than the configured telemetry, is made available to Advanced Diagnostic System (ADS) clients running on wireless handheld devices, affording the crew greater mobility for troubleshooting and providing improved insight into vehicle state. The superset of diagnostic data is made available to the ground in near real-time via an out-of band downlink, providing a high level of fidelity between vehicle state and test, training and operational facilities on the ground.

Fletcher, Daryl P.↗
NASA NTRS2013

A Framework to Debug Diagnostic Matrices

Diagnostics is an important concept in system health and monitoring of space operations. Many of the existing diagnostic algorithms utilize system knowledge in the form of diagnostic matrix (D-matrix, also popularly known as diagnostic dictionary, fault signature matrix or reachability matrix) gleaned from physical models. But, sometimes, this may not be coherent to obtain high diagnostic performance. In such a case, it is important to modify this D-matrix based on knowledge obtained from other sources such as time-series data stream (simulated or maintenance data) within the context of a framework that includes the diagnostic/inference algorithm. A systematic and sequential update procedure, diagnostic modeling evaluator (DME) is proposed to modify D-matrix and wrapper logic considering least expensive solution first. This iterative procedure includes conditions ranging from modifying 0s and 1s in the matrix, or adding/removing the rows (failure sources) columns (tests). We will experiment this framework on datasets from DX challenge 2009.

Model-based↗
NASA NTRS1988

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

Stclair, D. C.↗