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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Neural Networks Analyze Data In Particle-Impact-Noise Tests

Electronic neural networks and computers put to use in analyzing data acquired in particle-impact-noise-detection (PIND) tests of packaged electronic components. PIND tests detect loose particles in packages that cause failures during subsequent operation of packages in presence of accelerations or other effects - for example, loose electrically conductive particles that bounce into positions in which they cause short circuits. Interpretation of test data more objective and accurate. Preliminary results suggest use of neural networks result in significant improvement in quality and reliability and decrease in cost of PIND testing.

Scaglione, Lois J.↗
NASA NTRS2000

A Nuclear Interaction Model for Understanding Results of Single Event Testing with High Energy Protons

An internuclear cascade and evaporation model has been adapted to estimate the LET spectrum generated during testing with 200 MeV protons. The model-generated heavy ion LET spectrum is compared to the heavy ion LET spectrum seen on orbit. This comparison is the basis for predicting single event failure rates from heavy ions using results from a single proton test. Of equal importance, this spectra comparison also establishes an estimate of the risk of encountering a failure mode on orbit that was not detected during proton testing. Verification of the general results of the model is presented based on experiments, individual part test results, and flight data. Acceptance of this model and its estimate of remaining risk opens the hardware verification philosophy to the consideration of radiation testing with high energy protons at the board and box level instead of the more standard method of individual part testing with low energy heavy ions.

Culpepper, William X.↗
NASA NTRS2002

Flight Test of Propulsion Monitoring and Diagnostic System

The objective of this program was to perform flight tests of the propulsion monitoring and diagnostic system (PMDS) technology concept developed by Honeywell under the NASA Advanced General Aviation Transport Experiment (AGATE) program. The PMDS concept is intended to independently monitor the performance of the engine, providing continuous status to the pilot along with warnings if necessary as well as making the data available to ground maintenance personnel via a special interface. These flight tests were intended to demonstrate the ability of the PMDS concept to detect a class of selected sensor hardware failures, and the ability to successfully model the engine for the purpose of engine diagnosis.

Gabel, Steve↗
NASA NTRS2003

System Modeling and Diagnostics for Liquefying-Fuel Hybrid Rockets

A Hybrid Combustion Facility (HCF) was recently built at NASA Ames Research Center to study the combustion properties of a new fuel formulation that burns approximately three times faster than conventional hybrid fuels. Researchers at Ames working in the area of Integrated Vehicle Health Management recognized a good opportunity to apply IVHM techniques to a candidate technology for next generation launch systems. Five tools were selected to examine various IVHM techniques for the HCF. Three of the tools, TEAMS (Testability Engineering and Maintenance System), L2 (Livingstone2), and RODON, are model-based reasoning (or diagnostic) systems. Two other tools in this study, ICS (Interval Constraint Simulator) and IMS (Inductive Monitoring System) do not attempt to isolate the cause of the failure but may be used for fault detection. Models of varying scope and completeness were created, both qualitative and quantitative. In each of the models, the structure and behavior of the physical system are captured. In the qualitative models, the temporal aspects of the system behavior and the abstraction of sensor data are handled outside of the model and require the development of additional code. In the quantitative model, less extensive processing code is also necessary. Examples of fault diagnoses are given.

Poll, Scott↗
NASA NTRS2005

Lessons Learned in the Livingstone 2 on Earth Observing One Flight Experiment

The Livingstone 2 (L2) model-based diagnosis software is a reusable diagnostic tool for monitoring complex systems. In 2004, L2 was integrated with the JPL Autonomous Sciencecraft Experiment (ASE) and deployed on-board Goddard's Earth Observing One (EO-1) remote sensing satellite, to monitor and diagnose the EO-1 space science instruments and imaging sequence. This paper reports on lessons learned from this flight experiment. The goals for this experiment, including validation of minimum success criteria and of a series of diagnostic scenarios, have all been successfully net. Long-term operations in space are on-going, as a test of the maturity of the system, with L2 performance remaining flawless. L2 has demonstrated the ability to track the state of the system during nominal operations, detect simulated abnormalities in operations and isolate failures to their root cause fault. Specific advances demonstrated include diagnosis of ambiguity groups rather than a single fault candidate; hypothesis revision given new sensor evidence about the state of the system; and the capability to check for faults in a dynamic system without having to wait until the system is quiescent. The major benefits of this advanced health management technology are to increase mission duration and reliability through intelligent fault protection, and robust autonomous operations with reduced dependency on supervisory operations from Earth. The work-load for operators will be reduced by telemetry of processed state-of-health information rather than raw data. The long-term vision is that of making diagnosis available to the onboard planner or executive, allowing autonomy software to re-plan in order to work around known component failures. For a system that is expected to evolve substantially over its lifetime, as for the International Space Station, the model-based approach has definite advantages over rule-based expert systems and limit-checking fault protection systems, as these do not scale well. The model-based approach facilitates reuse of the L2 diagnostic software; only the model of the system to be diagnosed and telemetry monitoring software has to be rebuilt for a new system or expanded for a growing system. The hierarchical L2 model supports modularity and expendability, and as such is suitable solution for integrated system health management as envisioned for systems-of-systems.

Hayden, Sandra C.↗
NASA NTRS2010

The Application of Ultrasonic Inspection to Crimped Electrical Connections

The development of a new ultrasonic measurement technique to quantitatively assess wire crimp terminations is discussed. The development of a prototype instrument, based on a modified, commercially available, crimp tool, is demonstrated for applying this technique when wire crimps are installed. The crimp tool has three separate crimping locations that accommodate the three different ferrule diameters. The crimp tool in this study is capable of crimping wire diameters ranging from 12 to 26 American Wire Gauge (AWG). A transducer design is presented that allows for interrogation of each of the three crimp locations on the crimp tool without reconfiguring the device. An analysis methodology, based on transmitted ultrasonic energy and timing of the first received pulse is shown to correlate to both crimp location in the tool and the AWG of the crimp/ferrule combination. The detectability of a number of the crimp failure pathologies, such as missing strands, partially inserted wires and incomplete crimp compression, is discussed. A wave propagation model, solved by finite element analysis, describes the compressional ultrasonic wave propagation through the junction during the crimping process.

Cramer, K. Elliott↗
NASA NTRS2004

Analysis of SSEM Sensor Data Using BEAM

A report describes analysis of space shuttle main engine (SSME) sensor data using Beacon-based Exception Analysis for Multimissions (BEAM) [NASA Tech Briefs articles, the two most relevant being Beacon-Based Exception Analysis for Multimissions (NPO- 20827), Vol. 26, No.9 (September 2002), page 32 and Integrated Formulation of Beacon-Based Exception Analysis for Multimissions (NPO- 21126), Vol. 27, No. 3 (March 2003), page 74] for automated detection of anomalies. A specific implementation of BEAM, using the Dynamical Invariant Anomaly Detector (DIAD), is used to find anomalies commonly encountered during SSME ground test firings. The DIAD detects anomalies by computing coefficients of an autoregressive model and comparing them to expected values extracted from previous training data. The DIAD was trained using nominal SSME test-firing data. DIAD detected all the major anomalies including blade failures, frozen sense lines, and deactivated sensors. The DIAD was particularly sensitive to anomalies caused by faulty sensors and unexpected transients. The system offers a way to reduce SSME analysis time and cost by automatically indicating specific time periods, signals, and features contributing to each anomaly. The software described here executes on a standard workstation and delivers analyses in seconds, a computing time comparable to or faster than the test duration itself, offering potential for real-time analysis.

Zak, Michail↗
NASA NTRS2008

PH and Electrochemical Responsive Materials for Corrosion Smart Coating Applications

Corrosion is a costly issue for military operations and civil industries. While most corrosion initiates from localized corrosion form, such as pitting, failure directly caused by localized corrosion is the most dangerous kind, because it is difficult to anticipate and prevent, occurs very suddenly and can be catastrophic. One way of preventing these failures is with a coating that can detect and heal localized corrosion. pH and other electrochemical changes are often associated with localized corrosion, so it is expected that materials that are pH or otherwise electrochemical responsive can be used to detect and control corrosion. This paper will review various pH and electrochemical responsive materials and their potential applications in corrosion smart coatings. Current research results in this field will also be reported.

Li, Wenyan↗
NASA NTRS2017

Data Collection and Analysis Software Development for Rotor Dynamics Testing in Spin Laboratory

Gas turbine engine components undergo high rotational loading another complex environmental conditions. Such operating environment leads these components to experience damages and cracks that can cause catastrophic failure during flights. There are traditional crack detections and health monitoring methodologies currently being used which rely on periodic routine maintenances, nondestructive inspections that often times involve engine and components dis-assemblies. These methods do not also offer adequate information about the faults, especially, if these faults at subsurface or not clearly evident. At NASA Glenn research center, the rotor dynamics laboratory is presently involved in developing newer techniques that are highly dependent on sensor technology to enable health monitoring and prediction of damage and cracks in rotor disks. These approaches are noninvasive and relatively economical. Spin tests are performed using a subscale test article mimicking turbine rotor disk undergoing rotational load. Non-contact instruments such as capacitive and microwave sensors are used to measure the blade tip gap displacement and blade vibrations characteristics in an attempt develop a physics based model to assess/predict the faults in the rotor disk. Data collection is a major component in this experimental-analytical procedure and as a result, an upgrade to an older version of the data acquisition software which is based on LabVIEW program has been implemented to support efficiently running tests and analyze the results. Outcomes obtained from the tests data and related experimental and analytical rotor dynamics modeling including key features of the updated software are presented and discussed.

Rotor Dynamics↗
NASA NTRS2019

SAFE50 Reference Design Study for Large-Scale High-Density Low-Altitude UAS Operations in Urban Areas

Enabling safe, routine, and high-density flight operations of small UAS at low-altitude over heavily populated urban centers presents a difficult challenge for emerging UAS Traffic Management (UTM) system concepts. Urban operations by definition involve flight over people, property, and infrastructure. Low-altitude urban environments - such as urban canyons – are one of the most difficult areas for UTM to consider. Mission concepts require routine operations in a cluttered radio-frequency (RF) environment with degraded or denied Global Positioning System (GPS) reception. Flights with any appreciable distance will be beyond visual and communications line-of-sight from ground operators. Timely detection and response to emergencies and onboard failures, which is critical for safe aircraft operation, will be difficult. This work seeks to establish a feasible reference autonomy architecture for autonomous vehicles in an urban UTM system, then verifying and validating this architecture within a complete UTM concept point-design and systems analysis study. In this paper, we present the results from the NASA SAFE50 conceptual design and systems study that investigates the trade-space of urban UTM operations. This advanced conceptual design study develops a feasible, verified, validated point-design solution. The SAFE50 point-design concept places emphasis on advanced, highly-autonomous, and highly-capable vehicles that favors intelligent onboard autonomy over direct human control with today's technologies and operating in today's urban environments. This paper focuses on an general overview of the design study, highlighting decisions made in the architectural solution. This paper will presents a summary of the study, architectures, and requirements. We present an overview of the architecture designs as derived from the top-level UTM system. The point-design has been implemented in both simulation and through flight testing of hardware design prototypes. The results from simulation and flight testing as part of the verification and validation process of the reference design study.

Ippolito, Corey A.↗
NASA NTRSDate not supplied

An Intern Experience with NASA’s Artemis Program

“3...2...1...Go For Launch”. Nasa’s Kennedy Space Center is projected to launch the first mission of theArtemis Program by the end of this year, 2021. I have had the opportunity to contribute directly to this mission through my internship with their Engineering organization from January to August of 2021.Within their Engineering organization, I am an intern for the Avionics branch. Throughout this experience I have completed two projects with one project still in progress. These projects allowed me to work directly with the Orion vehicle’s software and hardware power ups, checkouts, and tests. I became familiarized with the engineering concepts that allowed this advanced technology to perform as intended.In addition, I became acquainted with the operations and tasks that are completed during a LaunchCountdown, both at a detailed level as well as a higher managerial level. Lastly, I worked directly with the software algorithm that detects and responds to onboard faults and failures. I have analyzed and made conclusions based on data and documentation, allowing my team to benefit from a condensed, centralized informational file. In addition to my technical experience, I have learned a significant amount about how the Kennedy Space Center operates, it’s history, and the history of the agency. The education I received from the Engineering department at Texas A&M University not only allowed me to obtain this internship, but provided me with the required background in computer and electrical engineering principles to be successful in this organization.

Sarah Bianco↗
NASA NTRS2010

General Purpose Data-Driven Online System Health Monitoring with Applications to Space Operations

Modern space transportation and ground support system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional parameter limit checking, or model-based or rule-based methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults, failures, or precursors of significant failures. The Inductive Monitoring System (IMS) is a general purpose, data-driven system health monitoring software tool that has been successfully applied to several aerospace applications and is under evaluation for anomaly detection in vehicle and ground equipment for next generation launch systems. After an introduction to IMS application development, we discuss these NASA online monitoring applications, including the integration of IMS with complementary model-based and rule-based methods. Although the examples presented in this paper are from space operations applications, IMS is a general-purpose health-monitoring tool that is also applicable to power generation and transmission system monitoring.

Iverson, David L.↗
NASA NTRS2001

Automating Trend Analysis for Spacecraft Constellations

Spacecraft trend analysis is a vital mission operations function performed by satellite controllers and engineers, who perform detailed analyses of engineering telemetry data to diagnose subsystem faults and to detect trends that may potentially lead to degraded subsystem performance or failure in the future. It is this latter function that is of greatest importance, for careful trending can often predict or detect events that may lead to a spacecraft's entry into safe-hold. Early prediction and detection of such events could result in the avoidance of, or rapid return to service from, spacecraft safing, which not only results in reduced recovery costs but also in a higher overall level of service for the satellite system. Contemporary spacecraft trending activities are manually intensive and are primarily performed diagnostically after a fault occurs, rather than proactively to predict its occurrence. They also tend to rely on information systems and software that are oudated when compared to current technologies. When coupled with the fact that flight operations teams often have limited resources, proactive trending opportunities are limited, and detailed trend analysis is often reserved for critical responses to safe holds or other on-orbit events such as maneuvers. While the contemporary trend analysis approach has sufficed for current single-spacecraft operations, it will be unfeasible for NASA's planned and proposed space science constellations. Missions such as the Dynamics, Reconnection and Configuration Observatory (DRACO), for example, are planning to launch as many as 100 'nanospacecraft' to form a homogenous constellation. A simple extrapolation of resources and manpower based on single-spacecraft operations suggests that trending for such a large spacecraft fleet will be unmanageable, unwieldy, and cost-prohibitive. It is therefore imperative that an approach to automating the spacecraft trend analysis function be studied, developed, and applied to missions such as DRACO with the intent that mission operations costs be significantly reduced. The goal of the Constellation Spacecraft Trend Analysis Toolkit (CSTAT) project is to serve as the pathfinder for a fully automated trending system to support spacecraft constellations. The development approach to be taken is evolutionary. In the first year of the project, the intent is to significantly advance the state of the art in current trending systems through improved functionality and increased automation. In the second year, the intent is to add an expert system shell, likely through the adaptation of an existing commercial-off-the-shelf (COTS) or government-off-the-shelf (GOTS) tool to implement some level of the trending intelligence that humans currently provide in manual operations. In the third year, the intent is to infuse the resulting technology into a near-term constellation or formation-flying mission to test it and gain experience in automated trending. The lessons learned from the real missions operations experience will then be used to improve the system, and to ultimately incorporate it into a fully autonomous, closed-loop mission operations system that is truly capable of supporting large constellations. In this paper, the process of automating trend analysis for spacecraft constellations will be addressed. First, the results of a survey on automation in spacecraft mission operations in general, and in trending systems in particular will be presented to provide an overview of the current state of the art. Next, a rule-based model for implementing intelligent spacecraft subsystem trending will be then presented, followed by a survey of existing COTS/GOTS tools that could be adapted for implementing such a model. The baseline design and architecture of the CSTAT system will be presented. Finally, some results obtained from initial software tests and demonstrations will be presented.

Davis, George↗
NASA NTRS1992

Acoustic emission monitoring of low velocity impact damage in graphite/epoxy laminates during tensile loading

An acoustic emission (AE) system was set up in a linear location data acquisition mode to monitor the tensile loading of eight-ply quasi-isotropic graphite/epoxy specimens containing low velocity impact damage. The impact damage was induced using an instrumented drop weight tower. During impact, specimens were supported by either an aluminum plate or a membrane configuration. Cross-sectional examinations revealed that the aluminum plate configuration resulted in primarily matrix cracking and back surface fiber failure. The membrane support resulted in only matrix cracking and delamination damage. Penetrant enhanced radiography and immersion ultrasonics were used in order to assess the amount of impact damage in each tensile specimen. During tensile loading, AE reliably detected and located the damage sites which included fiber failure. All specimens with areas of fiber breakage ultimately failed at the impact site. AE did not reliably locate damage which consisted of only delaminations and matrix cracking. Specimens with this type of damage did not ultimately fail at the impact site. In summary, AE demonstrated the ability to increase the reliability of structural proof tests; however, the successful use of this technique requires extensive baseline testing.

Parker, Bradford H.↗
NASA NTRS1976

Specific failure identification algorithms for the F-8

Analytical redundancy is applied to the pitch axis of an aircraft to enhance sensor fault detection. Three different designs based on monitoring Kalman filter residuals or likelihood functions were developed. Sensor failures were simulated to evaluate the performance of the algorithms on a nonlinear simulation of an F-8C aircraft. Based on requirements for sensor fault detection and preliminary simulator performance it is concluded that these algorithms have the potential for reducing the level of sensor redundancy in a digital flight control system without compromising performance.

Hartmann, G. L.↗
NASA NTRSDate not supplied

Testing of a Multi-Stringer Post-Buckled Panel with Incremental Damage Detection

A multi-stringer panel was designed and tested to produce a failure mode of skin-stiffener delamination due to compression loading of a post-buckled skin to support the validation of high-fidelity progressive damage failure analysis modeling. The test procedure included multiple methods of data acquisition ranging from multiple strain gages, front and back digital image correlation, passive thermography, acoustic emission, and ultrasonic inspection to capture key events and damage progression prior to catastrophic failure. Detailed data showing the initial stiffness, onset of skin buckling, buckled mode shape, damage initiation load, damage propagation morphology, and peak load was captured. Two panel configurations were tested. The first had a Teflon insert placed in the interface between the skin and the stiffener flanges adjacent to the center skin bay. The second was subjected to impact damage to the skin-stiffener flange. In each case, continuously-loaded-to-failure tests were conducted first with passive thermography being used exclusively to determine key damage events. Then, incrementally loaded tests were completed where ultrasonic inspections were conducted between each increment. The amount of load applied between each increment was determined based on the in-situ damage detection provided by the passive thermography system. The results of the testing showed a complex delamination growth pattern which migrated between ply interfaces through matrix splits in the upper skin ply of the panel. The result of the testing produced very detailed data which will prove to be useful for validation of delamination growth and migration predictions from progressive damage failure analysis methods.

Jason Action↗
NASA NTRS1986

A Sensor Failure Simulator for Control System Reliability Studies

A real-time Sensor Failure Simulator (SFS) was designed and assembled for the Advanced Detection, Isolation, and Accommodation (ADIA) program. Various designs were considered. The design chosen features an IBM-PC/XT. The PC is used to drive analog circuitry for simulating sensor failures in real-time. A user defined scenario describes the failure simulation for each of the five incoming sensor signals. Capabilities exist for editing, saving, and retrieving the failure scenarios. The SFS has been tested closed-loop with the Controls Interface and Monitoring (CIM) unit, the ADIA control, and a real-time F100 hybrid simulation. From a productivity viewpoint, the menu driven user interface has proven to be efficient and easy to use. From a real-time viewpoint, the software controlling the simulation loop executes at greater than 100 cycles/sec.

Melcher, K. J.↗