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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Discovering operating modes in telemetry data from the Shuttle Reaction Control System

This paper addresses the problem of detecting and diagnosing faults in physical systems, for which suitable system models are not available. An architecture is proposed that integrates the on-line acquisition and exploitation of monitoring and diagnostic knowledge. The focus is on the component of the architecture that discovers classes of behaviors with similar characteristics by observing a system in operation. A characterization of behaviors based on best fitting approximation models is investigated. An experimental prototype has been implemented to test it. Preliminary results in diagnosing faults of the reaction control system of the space shuttle are presented. The merits and limitations of the approach are identified and directions for future work are set.

Manganaris, Stefanos↗
NASA NTRS1993

Towards a machine learning framework for acquiring and exploiting monitoring and diagnostic knowledge

In this paper we address the problem of detecting and diagnosing faults in physical systems, for which neither prior expertise for the task nor suitable system models are available. We propose an architecture that integrates the on-line acquisition and exploitation of monitoring and diagnostic knowledge. The focus of the paper is on the component of the architecture that discovers classes of behaviors with similar characteristics by observing a system in operation. We investigate a characterization of behaviors based on best fitting approximation models. An experimental prototype has been implemented to test it. We present preliminary results in diagnosing faults of the Reaction Control System of the Space Shuttle. The merits and limitations of the approach are identified and directions for future work are set.

Manganaris, Stefanos↗
DOE OSTI2021

Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault’s location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.

58 GEOSCIENCES↗
NASA NTRS2005

Intelligent Engine Systems Work Element 1.3: Sub System Health Management

The objectives of this program were to develop health monitoring systems and physics-based fault detection models for engine sub-systems including the start, lubrication, and fuel. These models will ultimately be used to provide more effective sub-system fault identification and isolation to reduce engine maintenance costs and engine down-time. Additionally, the bearing sub-system health is addressed in this program through identification of sensing requirements, a review of available technologies and a demonstration of a demonstration of a conceptual monitoring system for a differential roller bearing. This report is divided into four sections; one for each of the subtasks. The start system subtask is documented in section 2.0, the oil system is covered in section 3.0, bearing in section 4.0, and the fuel system is presented in section 5.0.

Ashby, Malcolm↗
NASA NTRS2011

Recommendations for Enabling Manual Component Level Electronic Repair for Future Space Missions

Long duration missions to the Moon and Mars pose a number of challenges to mission designers, controllers, and the crews. Among these challenges are planning for corrective maintenance actions which often require a repair. Current repair strategies on the International Space Station (ISS) rely primarily on the use of Orbital Replacement Units (ORUs), where a faulty unit is replaced with a spare, and the faulty unit typically returns to Earth for analysis and possible repair. The strategy of replace to repair has posed challenges even for the ISS program. Repairing faulty hardware at lower levels such as the component level can help maintain system availability in situations where no spares exist and potentially reduce logistic resupply mass.This report provides recommendations to help enable manual replacement of electronics at the component-level for future manned space missions. The recommendations include hardware, tools, containment options, and crew training. The recommendations are based on the work of the Component Level Electronics Assembly Repair (CLEAR) task of the Exploration Technology Development Program from 2006 to 2009. The recommendations are derived based on the experience of two experiments conducted by the CLEAR team aboard the International Space Station as well as a group of experienced Miniature/Microminiature (2M) electronics repair technicians and instructors from the U.S. Navy 2M Project Office. The emphasis of the recommendations is the physical repair. Fault diagnostics and post-repair functional test are discussed in other CLEAR reports.

Struk, Peter M.↗
NASA NTRS2013

Physics Based Model for Online Fault Detection in Autonomous Cryogenic Loading System

We report the progress in the development of the chilldown model for rapid cryogenic loading system developed at KSC. The nontrivial characteristic feature of the analyzed chilldown regime is its active control by dump valves. The two-phase flow model of the chilldown is approximated as one-dimensional homogeneous fluid flow with no slip condition for the interphase velocity. The model is built using commercial SINDAFLUINT software. The results of numerical predictions are in good agreement with the experimental time traces. The obtained results pave the way to the application of the SINDAFLUINT model as a verification tool for the design and algorithm development required for autonomous loading operation.

Cryogenics↗
DOE OSTI2024

Seismic Features Predict Ground Motions During Repeating Caldera Collapse Sequence

Abstract Applying machine learning to continuous acoustic emissions, signals previously deemed noise, from laboratory faults and slowly slipping subduction‐zone faults, demonstrates hidden signatures are emitted that describe physical details, including fault displacement and friction. However, no evidence currently exists to demonstrate that similar hidden signals occur during seismogenic stick‐slip on earthquake faults—the damaging earthquakes of most societal interest. We show that continuous seismic emissions emitted during the 2018 multi‐month caldera collapse sequence at the Kı̄lauea volcano in Hawai'i contain hidden signatures characterizing the earthquake cycle. Multi‐spectral data features extracted from 30 s intervals of the continuous seismic emission are used to train a gradient boosted tree regression model to predict the GNSS‐derived contemporaneous surface displacement and time‐to‐failure of the upcoming collapse event. This striking result suggests that at least some faults emit such signals and provide a potential path to characterizing the instantaneous and future behavior of earthquake faults.

58 GEOSCIENCES↗
DOE OSTI2023

Hybrid Attack Graph Generation with Graph Convolutional Deep-Q Learning

Critical infrastructures such as power grids have become increasingly complex, connected, and vulnerable to adverse scenarios, including cyber and physical attacks and faults. Effective risk mitigation for such cyber-physical energy systems (CPES), requires preemptive knowledge of likely adversarial attack scenarios. Hybrid Attack Graph (HAG) is a structured way to represent an adversarial scenario as an attack sequence using a threat model. However, the scarcity of documented attack sequences hinders analysts and CPES planners’ ability to identify credible attack scenarios for a given CPES. We propose a data-driven Graph Convolutional Deep-Q Network (GCDQ) to address this data challenge through generating HAGs. By leveraging limited real-world observations from the MITRE ATT&CK knowledge base, our GCDQ model synthesizes realistic graphs with the targeted attribute of minimum detectability via reinforcement learning. This generative model is the first step in creating a tool to substantially boost the attack sequence dataset and enhance the performance of CPS defense-related tasks by providing insights into likely attack sequences with given attributes.

deep learning, artificial intelligence↗
DOE OSTI2026

A Physics-Aligned Multi-Domain Machine Learning Framework for Time-Localised Diagnosis of Power Electronics Faults

This paper presents a physics-aligned framework for fault diagnosis in multi-phase power-electronic systems using cycle-synchronous windowing and multi-domain features derived from Fourier, wavelet, and Hilbert–Huang representations. While both logistic regression and multilayer perceptron (MLP) models achieve perfect performance under standard evaluation, blind unseen testing reveals a critical failure in a baseline MLP. This is shown to arise from model selection based on validation accuracy. Using validation-loss-based selection restores correct unseen performance and improves confidence. Feature ablation shows that Fourier and wavelet features dominate, while computational analysis indicates that feature extraction, particularly HHT, governs runtime.

Kumar, Praveen [ORNL] (ORCID:0000000291877857)↗
DOE OSTI2023

Resilient Communication Scheme for Distributed Decision of Interconnecting Networks of Microgrids

Networking of microgrids can provide the operational flexibility needed for the increasing number of DERs deployed at the distribution level and supporting end-use demand when there is loss of the bulk power system. But, networked microgrids are vulnerable to cyber-physical attacks and faults due to the complex interconnections. As such, it is necessary To design resilient control systems to support the operations of networked microgrids in responses to cyber-physical attacks and faults. This paper introduces a resilient communication scheme for interconnecting multiple microgrids to support critical demand, in which the interconnection decision can be made distributedly by each microgrid controller even in the presence of cyberattacks to some communication links or microgrid controllers. This scheme blends a randomized peer-to-peer communication network for exchanging information among controllers and resilient consensus algorithms for achieving reliable interconnection agreement. The network of 6 microgrids divided from a modified 123-node test distribution feeder is used to demonstrate the effectiveness of the proposed resilient communication scheme.

Vu, Thanh Long↗
DOE OSTI2023

Physics-informed machine learning for fault-leakage reduced-order modeling

Geologic carbon storage (GCS) is a promising technology for mitigating CO 2 emissions. The overall success of GCS depends on safe operations that are informed by risk assessment and have proper mitigation plans in place. Performing quantitative probabilistic risk assessment for a GCS site using traditional reservoir simulators can be challenging due to the high computational costs. To overcome this challenge, the US Department of Energy’s National Risk Assessment Partnership (NRAP) project has developed an integrated assessment modeling approach that utilizes computationally efficient reduced-order models (ROM) for simulating various parts of a GCS storage site to quantify uncertainty. Here, in this study, we develop a reduced-order model for fault leakage risk assessment. We use a deep learning approach to build the reduced-order model. We perform a sensitivity analysis and find that the deep learning model yields high accuracy with a much smaller computational cost than full-physics simulation. We also evaluate the performance of the model in scenarios where simulations are not possible to run, providing analysis not previously performed in fault-leakage ROM analyses. Based on a sensitivity analysis of the model, we suggest a simplified conceptual model for fault leakage and site monitoring.

58 GEOSCIENCES↗
NASA NTRS1995

Earthquake recurrence on the southern San Andreas modulated by fault-normal stress

Earthquake recurrence data from the Pallett Creek and Wrightwood paleoseismic sites on the San Andreas fault appear to show temporal variations in repeat interval. We investigate the interaction between strike-slip faults and auxiliary reverse and normal faults as a physical mechanism capable of producing such variations. Under the assumption that fault strength is a function of fault-normal stress (e.g. Byerlee's Law), failure of an auxiliary fault modifies the strength of the strike-slip fault, thereby modulating the recurrence interval for earthquakes. In our finite element model, auxiliary faults are driven by stress accumulation near restraining and releasing bends of a strike-slip fault. Earthquakes occur when fault strength is exceeded and are incorporated as a stress drop which is dependent on fault-normal stress. The model is driven by a velocity boundary condition over many earthquake cycles. Resulting synthetic strike-slip earthquake recurrence data display temporal variations similar to observed paleoseismic data within time windows surrounding auxiliary fault failures. Our simple model supports the idea that interaction between a strike-slip fault and auxiliary reverse or normal faults can modulate the recurrence interval of events on the strike-slip fault, possibly producing short term variations in earthquake recurrence interval.

Palmer, Randy↗
NASA NTRS1986

Fault latency in the memory - An experimental study on VAX 11/780

Fault latency is the time between the physical occurrence of a fault and its corruption of data, causing an error. The measure of this time is difficult to obtain because the time of occurrence of a fault and the exact moment of generation of an error are not known. This paper describes an experiment to accurately study the fault latency in the memory subsystem. The experiment employs real memory data from a VAX 11/780 at the University of Illinois. Fault latency distributions are generated for s-a-0 and s-a-1 permanent fault models. Results show that the mean fault latency of a s-a-0 fault is nearly 5 times that of the s-a-1 fault. Large variations in fault latency are found for different regions in memory. An analysis of a variance model to quantify the relative influence of various workload measures on the evaluated latency is also given.

Chillarege, Ram↗
NASA NTRS1986

Fault and Error Latency Under Real Workload: an Experimental Study

A practical methodology for the study of fault and error latency is demonstrated under a real workload. This is the first study that measures and quantifies the latency under real workload and fills a major gap in the current understanding of workload-failure relationships. The methodology is based on low level data gathered on a VAX 11/780 during the normal workload conditions of the installation. Fault occurrence is simulated on the data, and the error generation and discovery process is reconstructed to determine latency. The analysis proceeds to combine the low level activity data with high level machine performance data to yield a better understanding of the phenomena. A strong relationship exists between latency and workload and that relationship is quantified. The sampling and reconstruction techniques used are also validated. Error latency in the memory where the operating system resides was studied using data on the physical memory access. Fault latency in the paged section of memory was determined using data from physical memory scans. Error latency in the microcontrol store was studied using data on the microcode access and usage.

Chillarege, Ram↗
NASA NTRS1989

A physical model for earthquakes. III - Thermodynamical approach and its relation to nonclassical theories

An earthquake theory based on fundamental thermodynamical considerations was developed using two principles. The first principle is that physical systems, including fault systems, tend toward a state of minimal Helmholtz free energy, which corresponds to static equilibrium. The second postulate is that, if the system is not at minimal free energy, thermodynamic forces arise which drive the system back toward a minimum. According to this theory, the propagation of a fracture, or the formation of a slip on a frictional surface, are manifestations of the physics which attempt to return the system to minimal free energy. It is shown that the process of return to minimal free energy by the system corresponds to the production of entropy, thereby rendering the process irreversible.

Rundle, John B.↗
NASA NTRS1992

A study of mapping exogenous knowledge representations into CONFIG

Qualitative reasoning is reasoning with a small set of qualitative values that is an abstraction of a larger and perhaps infinite set of quantitative values. The use of qualitative and quantitative reasoning together holds great promise for performance improvement in applications that suffer from large and/or imprecise knowledge domains. Included among these applications are the modeling, simulation, analysis, and fault diagnosis of physical systems. Several research groups continue to discover and experiment with new qualitative representations and reasoning techniques. However, due to the diversity of these techniques, it is difficult for the programs produced to exchange system models easily. The availability of mappings to transform knowledge from the form used by one of these programs to that used by another would open the doors for comparative analysis of these programs in areas such as completeness, correctness, and performance. A group at the Johnson Space Center (JSC) is working to develop CONFIG, a prototype qualitative modeling, simulation, and analysis tool for fault diagnosis applications in the U.S. space program. The availability of knowledge mappings from the programs produced by other research groups to CONFIG may provide savings in CONFIG's development costs and time, and may improve CONFIG's performance. The study of such mappings is the purpose of the research described in this paper. Two other research groups that have worked with the JSC group in the past are the Northwest University Group and the University of Texas at Austin Group. The former has produced a qualitative reasoning tool named SIMGEN, and the latter has produced one named QSIM. Another program produced by the Austin group is CC, a preprocessor that permits users to develop input for eventual use by QSIM, but in a more natural format. CONFIG and CC are both based on a component-connection ontology, so a mapping from CC's knowledge representation to CONFIG's knowledge representation was chosen as the focus of this study. A mapping from CC to CONFIG was developed. Due to differences between the two programs, however, the mapping transforms some of the CC knowledge to CONFIG as documentation rather than as knowledge in a form useful to computation. The study suggests that it may be worthwhile to pursue the mappings further. By implementing the mapping as a program, actual comparisons of computational efficiency and quality of results can be made between the QSIM and CONFIG programs. A secondary study may reveal that the results of the two programs augment one another, contradict one another, or differ only slightly. If the latter, the qualitative reasoning techniques may be compared in other areas, such as computational efficiency.

Mayfield, Blayne E.↗