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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Characterization and Comparison of Vibration Transfer Paths in a Helicopter Gearbox and a Fixture Mounted Gearbox

Health monitoring of rotorcraft components, currently being performed by Health and Usage Monitoring Systems through analyses of vibration signatures of dynamic mechanical components, is very important for their safe and economic operation. HUMS analyze vibration signatures associated with faults and quantify them as condition indicators to predict component behavior. Vibration transfer paths are characterized by frequency response functions derived from the input/output relationship between applied force and dynamic response through a structure as a function of frequency. With an objective to investigate the differences in transfer paths, transfer path measurements were recorded under similar conditions in the left and right nose gearboxes of an AH-64 helicopter and in an isolated left nose gearbox in a test fixture at NASA Glenn Research Center. The test fixture enabled the application of measured torques-common during an actual operation. An impact hammer as well as commercial and lab piezo shakers, were used in conjunction with two types of commercially available accelerometers to collect the vibration response under various test conditions. The frequency response functions measured under comparable conditions of both systems were found to be consistent. Measurements made on the fixture indicated certain real-world installation and maintenance issues, such as sensor alignments, accelerometer locations and installation torques, had minimal effect. However, gear vibration transfer path dynamics appeared to be somewhat dependent on the presence of oil, and the transfer path dynamics were notably different if the force input was on the internal ring gear rather than on the external gearbox case.

structures↗
NASA NTRS2018

Assurance of Model-Based Fault Diagnosis

Autonomy is an increasingly important technology for robotic scientific and commercial spacecraft. An important motivation for developing onboard autonomy is to enable quick response to dynamic environment and situations, including fault conditions that a spacecraft may encounter. The reliability of such autonomous capabilities depends on the quality of their knowledge of a spacecraft’s health state. Model-based approaches to fault management, i.e. model-based fault diagnosis (MBFD), is one approach to continuously verify correct behavior in addition to diagnosing symptoms to estimate the spacecraft’s health state. The proper functioning of MBFD is dependent on 1) the quality of the model that is analyzed and compared to the outputs of onboard sensors to estimate the system’s health state, and 2) the correct functioning of the diagnosis engine that interrogates the model and compares its analysis to observed system behavior. We are currently developing Verification and Validation (V&V) approaches to provide the necessary confidence that MBFD systems are correctly estimating the health of on-board spacecraft components and systems. Our work is intended to narrow the gap between the rapidly maturing field of ModelBased System Engineering (including MBFD) and the less-well understood area of identifying and applying appropriate V&V techniques to MBFD. Our effort is investigating three areas: 1) developing V&V techniques for the diagnostic model, 2) developing V&V techniques for the diagnostic engine in isolation, and 3) developing V&V techniques for the diagnostic engine and model in combination. This paper describes the work we have completed in the first area. We describe our approach to selecting a system to be represented by a model, the approach to modeling the system, the verification approach we developed for the model, and the results of the verification activity. We conclude with a description of the work remaining in the last two areas, which will be addressed over the next two years.

Chung, Seung↗
DOE OSTI2024

Errant Beam Prognostics with Machine Leaning at SNS Accelerator

Particle Accelerators are complex machine with many pieces of equipment running in synchronization to deliver required beam. However, faults in particle accelerators reduce the availability of the beam for experiments affecting the overall science output. To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability. Many researchers have adopted semi-supervised Machine Learning (ML) methods such as auto-encoders and variational auto-encoders for such tasks. However, supervised ML techniques designed for similarity learning such as Siamese Neural Network (SNN) can outperform semi-supervised or unsupervised methods for anomaly prediction. One of the challenges associated with application of ML models to particle accelerators is the variability in observed data over time due to system configuration changes. We employ conditional models such as Conditional Siamese Neural Networks (CSNN), and Conditional-VAE (CVAE) to learn the variability in the data by using beam configuration parameters as conditional input. We apply these models for errant beam prediction at Spallation Neutron Source accelerator under different system configurations and compare their performance. We demonstrate that CSNN outperforms CVAE in our application. This talk will present the data source, collection, analysis, data-preparation, model development, hyper-parameter studies and the results.

Rajput, Kishansingh↗
NASA NTRS1992

An architecture for the development of real-time fault diagnosis systems using model-based reasoning

Presented here is an architecture for implementing real-time telemetry based diagnostic systems using model-based reasoning. First, we describe Paragon, a knowledge acquisition tool for offline entry and validation of physical system models. Paragon provides domain experts with a structured editing capability to capture the physical component's structure, behavior, and causal relationships. We next describe the architecture of the run time diagnostic system. The diagnostic system, written entirely in Ada, uses the behavioral model developed offline by Paragon to simulate expected component states as reflected in the telemetry stream. The diagnostic algorithm traces causal relationships contained within the model to isolate system faults. Since the diagnostic process relies exclusively on the behavioral model and is implemented without the use of heuristic rules, it can be used to isolate unpredicted faults in a wide variety of systems. Finally, we discuss the implementation of a prototype system constructed using this technique for diagnosing faults in a science instrument. The prototype demonstrates the use of model-based reasoning to develop maintainable systems with greater diagnostic capabilities at a lower cost.

Hall, Gardiner A.↗
NASA NTRS1980

Validation Methods Research for Fault-Tolerant Avionics and Control Systems Sub-Working Group Meeting. CARE 3 peer review

A computer aided reliability estimation procedure (CARE 3), developed to model the behavior of ultrareliable systems required by flight-critical avionics and control systems, is evaluated. The mathematical models, numerical method, and fault-tolerant architecture modeling requirements are examined, and the testing and characterization procedures are discussed. Recommendations aimed at enhancing CARE 3 are presented; in particular, the need for a better exposition of the method and the user interface is emphasized.

Trivedi, K. S.↗
DOE OSTI2021

Deep Analysis Net with Causal Embedding for Coal-fired Power Plant Fault Detection and Diagnosis (DANCE4CFDD)

Fault detection and diagnosis is critical to power plant operation to ensure attaining high reliability while reducing operation cost. As more renewable power is introduced to the power grid, traditional fossil power plants take on the extra burden of excessive load cycling to compensate the generation variability from renewable power. Such load cycling will pose more reliability challenges to power plant operation. There are a number of challenges faced by today’s asset health management system in coal- fired (or gas) power plants: 1) high-dimensional nonlinear interaction among multiple time series measurements; 2) high measurement variance induced by operational conditions/modes; 3) variation among asset types and plant configurations; and 4) a small number of faulty events to learn from. To cope with these challenges, today’s fielded asset health management systems rely heavily on manual efforts from domain experts and hand-crafted features or rules based on domain knowledge. Despite its role in plant reliability, such a practice is costly and hinders its scalability and sustainability, particularly when a plant undergoes modifications. The objective of this project is to develop a novel end-to-end AI learning system that is trainable (i.e., the AI representation of a complex system behavior can be directly learned from properly labeled data) for accurate fault detection and root cause analysis. The ability to create a fault detection model directly from time series could alleviate the efforts associated with today’s asset management solution development. In the course of this project, we have achieved the following: Created an AI model development environment incorporating state-of-the-art neural network architectures for rapid model development and evaluation; Developed novel learning strategies for training of fault detection model; Developed special-purpose neural network architecture embedded with variable association graph aiming for better interpretability; Developed a learning strategy to leverage a small number of faulty events for enhanced fault detection capability; Conducted detailed experimental study based on public benchmark datasets and demonstrated the effectiveness of the proposed solution; and Validated the developed system with data from both a coal-fired plant boiler dynamic simulation model and real-world coal-fired power plant covering multiple asset and fault types. Overall, the project attained a technology readiness level of TRL 5 from TRL 2 at the beginning of the project.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2008

Exhaustive Thresholds and Resistance Checkpoints

Once deployed, all intricate systems that operate for a long time (such as an airplane or chemical processing plant) experience degraded performance during operational lifetime. These can result from losses of integrity in subsystems and parts that generally do not materially impact the operation of the vehicle (e.g., the light behind the button that opens the sliding door of the minivan). Or it can result from loss of more critical parts or subsystems. Such losses need to be handled quickly in order to avoid loss of personnel, mission, or part of the system itself. In order to manage degraded systems, knowledge of its potential problem areas and the means by which these problems are detected should be developed during the initial development of the system. Once determined, a web of sensors is employed and their outputs are monitored with other system parameters while the system is in preparation or operation. Just gathering the data is only part of the story. The interpretation of the data itself and the response of the system must be carefully developed as well to avoid a mishap. Typically, systems use a test-threshold-response paradigm to process potential system faults. However, such processing sub-systems can suffer from errors and oversights of a consistent type, causing system aberrant behavior instead of expected system and recovery operations. In our study, we developed a complete checklist for determining the completeness of a fault system and its robustness to common processing and response difficulties.

Easton, Charles↗
NASA NTRS2005

Fault-Detection Tool Has Companies 'Mining' Own Business

A successful launching of NASA's Space Shuttle hinges heavily on the three Space Shuttle Main Engines (SSME) that power the orbiter. These critical components must be monitored in real time, with sensors, and compared against expected behaviors that could scrub a launch or, even worse, cause in- flight hazards. Since 1981, SSME faults have caused 23 scrubbed launches and 29 percent of total Space Shuttle downtime, according to a compilation of analysis reports. The most serious cases typically occur in the last few seconds before ignition; a launch scrub that late in the countdown usually means a period of investigation of a month or more. For example, during the launch attempt of STS-41D in 1984, an anomaly was detected in the number three engine, causing the mission to be scrubbed at T-4 seconds. This not only affected STS-41D, but forced the cancellation of another mission and caused a 2-month flight delay. In 2002, NASA s Kennedy Space Center, the Florida Institute of Technology, and Interface & Control Systems, Inc., worked together to attack this problem by creating a system that could automate the detection of mechanical failures in the SSMEs fuel control valves.

Source record↗
NASA NTRS2024

Historical Aerospace Software Errors Categorized to Influence Fault Tolerance

Since the first use of computers in space and aircraft, software errors have occurred. These errors can manifest as loss-of-life or less catastrophically. As the demand for automation increases, software in mission or safety-critical systems should be designed to be tolerant to the most likely software faults. This paper categorizes a set of 55 historic aerospace software error incidents from 1962 to 2023 to determine trends of how and where automation is most likely to fail, behaving unexpectedly. A distinction between software producing unexpected (erroneous) output versus no output (failsilent) is introduced. Of the historical incidents analyzed, 85% were from software producing wrong output rather than simply stopping. Rebooting was found to be ineffective to clear erroneous behavior, and not reliable to recover from silent failures. Error origin was within the code/logic itself in 58% of cases, 16% from configurable data, 15% from unexpected sensor input, and 11% from command/operator input. A substantial forty percent (40%) of unexpected software behavior was indicated by the absence of code, arising from unanticipated situations and missing requirements, and 16% of incidents were subjectively deemed “unknown-unknowns”. No incidents were found to be the result of programming language, compiler, tool, or operating system; and only sixteen percent (16%) of all incidents were considered errors traditional computer science/programming in nature. These findings indicate that for fault tolerance, erroneous automation behavior must be a primary consideration especially at critical moments, and reboot recoverability may not be viable. Special care should be taken to validate configurable data and commands prior to use. “Test-like-you-fly”, including hardware-in-the-loop combined with robust off-nominal testing should be used to uncover missing logic arising from unanticipated situations not covered by requirements alone. This study uniquely focuses on manifestations of unexpected flight software behavior, independent of ultimate root cause. We characterize software error behavior and origin to improve software design, test, and operations for resilience to the most common manifestations, and provide a rich dataset for further study.

Aerospace↗
NASA NTRS1988

An assessment of AVIRIS data for hydrothermal alteration mapping in the Goldfield Mining District, Nevada

Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data were acquired over the Goldfield Mining District, Nevada, in September 1987. Goldfield is one of the group of large epithermal precious metal deposits in Tertiary volcanic rocks, associated with silicic volcanism and caldera formation. Hydrothermal alteration consists of silicification along fractures, advanced agrillic and argillic zones further away from veins and more widespread propylitic zones. An evaluation of AVIRIS data quality was performed. Faults in the data, related to engineering problems and a different behavior of the instrument while on-board the U2, were encountered. Consequently, a decision was made to use raw data and correct them only for dark current variations and detector read-out-delays. New software was written to that effect. Atmospheric correction was performed using the flat field correction technique. Analysis of the data was then performed to extract spectral information, mainly concentrating on the 2 to 2.45 micron window, as the alteration minerals of interest have their distinctive spectral reflectance features in this region. Principally kaolinite and alunite spectra were clearly obtained. Mapping of the different minerals and alteration zones was attempted using ratios and clustering techniques. Poor signal-to-noise performance of the instrument and the lack of appropriate software prevented the production of an alteration map of the area. Spectra extracted locally from the AVIRIS data were checked in the field by collecting representative samples of the outcrops.

Carrere, Veronique↗
NASA NTRS2006

A Case for Developing a Ground Based Replication of the Earth, Moon and Mars Spaceflight Infrastructure

When the systems are developed and in place to provide the services needed to operate en route and on the Lunar and Martian surfaces, an Earth based replication will need to be in place for the safety and protection of mission success. The replication will entail all aspects of the flight configuration end to end but will not include any closed loop systems. This would replicate the infrastructure from Lunar and Martian robots, manned surface excursions, through man and unmanned terrestrial bases, through the various types of communication systems and technologies, manned and un-manned space vehicles (large and small), to Earth based systems and control centers. An Earth based replicated infrastructure will enable checkout and test of new technologies, hardware, software updates and upgrades and procedures without putting humans and missions at risk. Analysis of events, what ifs and trouble resolution could be played out on the ground to remove as much risk as possible from any type of proposed change to flight operational systems. With adequate detail, it is possible that failures could be predicted with a high probability and action taken to eliminate failures. A major factor in any mission to the Moon and to Mars is the complexity of systems, interfaces, processes, their limitations, associated risks and the factor of the unknown including the development by many contractors and NASA centers. The need to be able to introduce new technologies over the life of the program requires an end to end test bed to analyze and evaluate these technologies and what will happen when they are introduced into the flight system. The ability to analyze system behaviors end to end under varying conditions would enhance safety e.g. fault tolerances. This analysis along with the ability to mine data from the development environment (e.g. test data), flight ops and modeling/simulations data would provide a level of information not currently available to operations and astronauts. In this paper we will analyze the beginnings of such a replication and what it could do in terms of reducing risk in the near term for development. We will analyze the Space Shuttle Main Engine (SSME) test lab which has to a large extent accomplished this replication for the SSME and has been highly successful in analyzing hardware and software problems and changes. The cost of replicating the flight system as proposed here could be very high if attempted as an afterthought. We will describe the initial steps for the development of a replication of this infrastructure starting with the communication infrastructure. The Constellation of Labs (CofL) under the Command, Control, Communication and Information (C3I) project for the NASA Exploration Initiative will provide the initial foundation upon which to base this replication. Simply put, there is very little margin for error in high latency situations e.g. en-route to/from Mars or in an autonomous process on the Lunar far side. Any thought out approach to reduce risk and increase safety needs to be accomplished end to end with the actual systems configuration.

Bradford, Robert N.↗
NASA NTRS2025

HabSim: A Modular Coupled Virtual Testbed for Simulating ExtraTerrestrial Habitat Systems

Extraterrestrial habitats involve a tightly coupled combination of hardware, software, and humans while operating in an unforgiving environment that poses many risks, both anticipated and unanticipated. Traditional approaches with such systems of systems focus on reliability, robustness, and redundancy. These approaches seek to avoid failure rather than reduce overall risk. However, faults are inevitable, and understanding and managing the complex and emergent behavior and cascading events of such a complex system is critical. This study describes the development of HabSim, a computational simulation environment intended to support research to establish the know-how to design and operate resilient and autonomous SmartHabs. HabSim is a modular virtual testbed composed of many of the coupled dynamic systems expected in a typical SmartHab. A heterogeneous set of interconnected physics-based and phenomenological models is used to represent the essential functions of a SmartHab. HabSim further considers disruptions and models damage and repair of certain components. This paper discusses a) system and subsystem requirements of the deep space habitat included in the HabSim platform; b) architectural choices made in response to the requirements; c) technical considerations for developing, verifying, configuring, and executing HabSim; and d) illustrative sample results from a simulation of a representative disruption scenario.

Mohsen Azimi↗
NASA NTRS1989

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

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

Malin, Jane T.↗
NASA NTRS2005

Inductive Learning Approaches for Improving Pilot Awareness of Aircraft Faults

Neural network flight controllers are able to accommodate a variety of aircraft control surface faults without detectable degradation of aircraft handling qualities. Under some faults, however, the effective flight envelope is reduced; this can lead to unexpected behavior if a pilot performs an action that exceeds the remaining control authority of the damaged aircraft. The goal of our work is to increase the pilot s situational awareness by informing him of the type of damage and resulting reduction in flight envelope. Our methodology integrates two inductive learning systems with novel visualization techniques. One learning system, the Inductive Monitoring System (IMS), learns to detect when a simulation includes faulty controls, while two others, Inductive Classification System (INCLASS) and multiple binary decision tree system (utilizing C4.5), determine the type of fault. In off-line training using only non-failure data, IMS constructs a characterization of nominal flight control performance based on control signals issued by the neural net flight controller. This characterization can be used to determine the degree of control augmentation required in the pitch, roll, and yaw command channels to counteract control surface failures. This derived information is typically sufficient to distinguish between the various control surface failures and is used to train both INCLASS and C4.5. Using data from failed control surface flight simulations, INCLASS and C4.5 independently discover and amplify features in IMS results that can be used to differentiate each distinct control surface failure situation. In real-time flight simulations, distinguishing features learned during training are used to classify control surface failures. Knowledge about the type of failure can be used by an additional automated system to alter its approach for planning tactical and strategic maneuvers. The knowledge can also be used directly to increase the pilot s situational awareness and inform manual maneuver decisions. Our multi-modal display of this information provides speech output to issue control surface failure warnings to a lesser-used communication channel and provides graphical displays with pilot-selectable !eve!s of details to issues additional information about the failure. We also describe a potential presentation for flight envelope reduction that can be viewed separately or integrated with an existing attitude indicator instrument. Preliminary results suggest that the inductive approach is capable of detecting that a control surface has failed and determining the type of fault. Furthermore, preliminary evaluations suggest that the interface discloses a concise summary of this information to the pilot.

Spikovska, Lilly↗
DOE OSTI2024

Velocity Dependence of Rate‐And‐State Friction in Granular Fault Gouge and Implications for Slow‐Slip Events

Abstract The standard rate‐and‐state friction (RSF) has extensively captured frictional behaviors, but it fails to explain the velocity dependence of frictional stability transition and widespread slow‐slip events (SSEs) in experiments and nature adequately. An alternative microphysical Chen‐Niemeijer‐Spiers (CNS) model can well describe the velocity dependence of frictional behaviors of granular gouges. Using the original CNS model, standard RSF parameters can be quantified microphysically. However, some micro‐parameters are not easy to estimate quantitatively, making it difficult to extrapolate to natural and experimental conditions. Here, we simplify the microphysically‐derived RSF parameters including direct effect a , evolution effect b , and critical slip distance D c , as well as equivalent values ( a eq , b eq , and D eq ). The simplified friction parameters directly illustrate their velocity dependence, namely the essentially constant a , a eq , and D c , negatively velocity‐dependent b and b eq , as well as varying D eq for different laws. They are roughly consistent with experimental results in various fault gouges. A modified CNS model is further derived from the original CNS model, establishing a direct link between the standard RSF and CNS models. The modified CNS model exhibits virtually identical frictional behaviors to the original CNS, but differs from the standard RSF at large velocity perturbations. Moreover, the linearized stability analysis indicates that the critical stiffness for the modified CNS model is velocity‐dependent. Compared with the standard RSF, the modified CNS model not only explains the velocity dependence of frictional stability transition, but also exhibits a more gradual transition for SSEs with a broader range of stiffness ratios.

Geochemistry & Geophysics↗
NASA NTRS1974

On-line diagnosis of unrestricted faults

A formal model for the study of on-line diagnosis is introduced and used to investigate the diagnosis of unrestricted faults. A fault of a system S is considered to be a transformation of S into another system S' at some time tau. The resulting faulty system is taken to be the system which looks like S up to time tau, and like S' thereafter. Notions of fault tolerance error are defined in terms of the resulting system being able to mimic some desired behavior as specified by a system similar to S. A notion of on-line diagnosis is formulated which involves an external detector and a maximum time delay within which every error caused by a fault in a prescribed set must be detected. It is shown that if a system is on-line diagnosable for the unrestricted set of faults then the detector is at least as complex, in terms of state set size, as the specification. The use of inverse systems for the diagnosis of unrestricted faults is considered. A partial characterization of those inverses which can be used for unrestricted fault diagnosis is obtained.

Meyer, J. F.↗
DOE OSTI2022

In-Situ Observation of FCC→HCP Transformation-Induced Plasticity Behavior During Dynamic Deformation of CoCrNi Multi-principal Element Alloys

CoCrNi multi-principal element alloys (MPEAs) have potential as high toughness, blast resistant materials, but limited studies of their high strain rate behavior have been performed. Their high toughness has been attributed to low stacking fault energies and the activation of toughness enhancing deformation mechanisms, such as twinning- and transformation-induced plasticity (TWIP and TRIP, respectively), that may be affected by increased strain rate. This work examines a series of CoCrNi MPEAs tested with a miniaturized pressure bar, while simultaneous synchrotron x-ray diffraction and imaging are performed, and compares these results to those collected during quasi-static deformation at elevated temperatures. Here, the collected diffraction indicates that TRIP was active at elevated strain rates for the most Co-rich alloys containing 50 or 55 at. pct. Co, while TRIP behavior was suppressed at elevated strain rates in a 40 at. pct. Co alloy anticipated to experience TRIP. The suppression of TRIP in the lower Co alloy at high strain rates is attributed to adiabatic heating, consistent with suppression of TRIP as a result of elevated temperatures.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Fault detection and diagnosis using neural network approaches

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Kramer, Mark A.↗