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At least 919 records · Page 51

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Uncovering Hazards Using a Multi-Objective Optimization to Explore the Faulty State-Space

Considering resilience when designing complex engineered systems is crucial to ensure the system is safe under unexpected hazardous scenarios. Traditional risk-based approaches, such as Failure Modes and Effects Analysis (FMEA) are useful for designing the system to mitigate hazardous scenarios that can be identified by the designer, but often require experience or prior knowledge of system failures to generate. More recently, researchers have developed simulation tools that enable the designer to model large sets of hazardous scenarios (driven by both internal faults and external factors) through simulation. While these tools enable a wider scope of fault modes to be evaluated (e.g., by injecting combined set of fault modes or injecting modes at different times), the resulting assessments (like FMEA) still require knowledge of the specific modes to be evaluated. However, failure to analyze a wide variety of fault scenarios can lead to an incomplete picture of the system resilience, especially to "surprise events'' which may be difficult for the designer to identify and predict beforehand. To overcome this challenge, previous work developed a fault sampling approach for resilience simulations which would procedurally-generate a wide variety of potential faults by systematically perturbing the health states of the system. While the resulting fault modes generated covered a much larger space hazards than would be otherwise considered (and identified many unique failure trajectories which would not have otherwise been identified), it also significantly increased the computational cost of the analysis and resulted in the simulation and analysis of a large set of essentially duplicate scenarios. Additionally, as the number of dimensions in the faulty state-space increases, the full elaboration of possible modes becomes computationally infeasible, justifying the use of a more targeted search. To resolve this limitation, this work proposes the use of a multiobjective optimization algorithm to search the health state space for potential fault modes that are both (1) hazardous and (2) unique. To solve this type of problem, this work proposes the use of a cooperative co-evolutionary algorithm. To demonstrate this approach, it will be applied to a model of an autonomous rover which uses line markings to navigate, focusing on potential hazards in the drive system which could cause the rover to crash. To determine the merit of the approach, it will further be compared with the previously-presented range elaboration approach and a random mode generation approach on the basis of computational efficiency and found modes.

Resilience↗

DOE OSTI2022

To Fail or not to Fail: An Exploration of Machine Learning Techniques for Predictive Maintenance

Predictive maintenance refers to the ability to predict when machinery or systems need to be maintained. Making an accurate prediction is quite challenging given the costs for both over-estimating (unnecessary maintenance and reduction in availability of assets) and under-estimating (untimely breakdowns and possible loss of equipment or lives). To address these challenges researchers were able to develop new approaches for analyzing oil samples taken extracting samples from oil-wetted machinery that may provide information critical to developing predictive capabilities. We consider the problem from both supervised (though data limited) and unsupervised approaches and provide a first look into a data driven approach for identification of condition indicators. Through this work we identify a collection of candidate features that can form the basis of condition indicators for both a high level discrimination of failure vs. normal operation as well as a set for potential failure mode identification. Finally, we present an anomaly detection framework for detecting failures which can be a viable solution for an onboard analysis tool in deployed systems.

predictive maintenance, anomaly detection, Laserne↗

DOE OSTI2016

SWiFT Hydraulic System FMEA

The purpose of this Failure Modes and Effects Analysis (FMEA) is chosen to examine the potential failures of the systems which could result in an overspeed event with a potential for flying debris from the wind turbine blades. The FMEA method was chosen to examine the turbine hydraulic system because two important turbine protective features use hydraulic pressure to function: the blade pitching system, and the brake. The objective of the FMEA was to determine if the two safety features have a likely common failure point, or if the safety systems can be individually credited for protection of the turbine.

17 WIND ENERGY↗

NASA NTRS1980

Bolt-bearing fatigue of a graphite/epoxy laminate

Graphite/epoxy laminates were tested under bolt-bearing loads for a range of bolt clampup torques and for several test conditions involving water. High clampup torque improved both the static strength and fatigue limit by about 100 percent compared to a simple pin-bearing case, which had no through-the-thickness constraint. The static strength improvement was explained in terms of failure modes. Bolt clampup also influenced the hole elongation that developed before failure. For both the static and fatigue tests, the hole elongations were much larger for the low-torque clampup. Tests in water degraded static bearing strength only slightly, but reduced the fatigue limit about 40 percent below that for tests conducted in air.

Crews, J. H., Jr.↗

NASA NTRS1981

Fatigue degradation in compressively loaded composite laminates

The effect of imbedded delaminations on the compression fatigue behavior of quasi-isotropic, T300/5208, graphite/epoxy laminates was investigated. Teflon imbedments were introduced during panel layup to create delaminations. Static and constant amplitude (R=10, omega = 10 Hz) fatigue tests were conducted. S-N data and half life residual strength data were obtained. During static compression loading, the maximum deflection of the buckled delaminated region was recorded. Under compression fatigue, growth of the imbedded delamination was identified as the predominant failure mode in most of the test cases. Specimens that exhibited others failures had a single low stiffness ply above the Teflon imbedment. Delamination growth during fatigue was monitored using DIB enhanced radiography. In specimens with buried delaminations, the dye penetrant (DIB) was introduced into the delaminated region through a minute laser drilled hole, using a hypodermic needle. A low kV, microfocus, X-ray unit was mounted near the test equipment to efficiently record the cyclic growth of buried delaminations on Polaroid film.

Ramkumar, R. L.↗

NASA NTRS1981

Bolt-bearing fatigue of a graphite/epoxy laminate

Graphite/epoxy laminates (T300/5208) were tested under bolt-bearing loads for a range of bolt clampup torques and for several test conditions involving water. High clampup torque improved both the static strength and fatigue limit by about 100 percent compared to a simple pin-bearing case, which had no through-the-thickness constraint. The static strength improvement was explained in terms of failure modes. Bolt clampup also influenced the hole elongation that developed before failure. For both the static and fatigue tests, the hole elongations were much larger for the low-torque-clampup case than for the pin-bearing case or for tests with moderate clampup torques. Tests in water degraded static bearing strength only slightly, but reduced the fatigue limit about 40 percent below that for tests conducted in air.

Crews, J. H., Jr.↗

NASA NTRS1983

Restructurable controls for aircraft

Future aircraft with highly sophisticated controls are likely to have multiple interdependent failure modes which will be difficult for the pilot to recognize. Such failures may lead to unanticipated sequences of events from which the pilot cannot intuitively recover. Advances in the state-of-the-art in failure detection, failure identification, and control system technology suggest it may be feasible to detect and identify potentially catastrophic failures in flight controls and to restructure the controls in real time to execute a safe landing. Two accidents are reviewed, one in which the pilot successfully restructured the controls and one in which he did not, but for which a solution existed. The problem requirements and potential theoretical techniques which apply are also discussed.

Howell, W. E.↗

NASA NTRS1983

Failure detection with uncertain models

In this paper the author considers the issue of robust failure detection. In one way or another all failure detection methods generate signals which tend to highlight the presence of particular failures if they have actually occurred. However, if any model uncertainties have effects on the observables which are at all like those of one or more of the failure modes, these will also be accentuated. Consequently the problem of robust failure detection is concerned with generating signals which are maximally sensitive to some effects (failures) and minimally sensitive to other (model errors). Previously announced in STAR as N83-32417

Lou, X.-C.↗

NASA NTRS1989

Model 0A wind turbine generator FMEA

The results of Failure Modes and Effects Analysis (FMEA) conducted for the Wind Turbine Generators are presented. The FMEA was performed for the functional modes of each system, subsystem, or component. The single-point failures were eliminated for most of the systems. The blade system was the only exception. The qualitative probability of a blade separating was estimated at level D-remote. Many changes were made to the hardware as a result of this analysis. The most significant change was the addition of the safety system. Operational experience and need to improve machine availability have resulted in subsequent changes to the various systems which are also reflected in this FMEA.

Klein, William E.↗

NASA NTRS1989

Thermal barrier coating life prediction model development, phase 1

The objective of this program was to establish a methodology to predict thermal barrier coating (TBC) life on gas turbine engine components. The approach involved experimental life measurement coupled with analytical modeling of relevant degradation modes. Evaluation of experimental and flight service components indicate the predominant failure mode to be thermomechanical spallation of the ceramic coating layer resulting from propagation of a dominant near interface crack. Examination of fractionally exposed specimens indicated that dominant crack formation results from progressive structural damage in the form of subcritical microcrack link-up. Tests conducted to isolate important life drivers have shown MCrAlY oxidation to significantly affect the rate of damage accumulation. Mechanical property testing has shown the plasma deposited ceramic to exhibit a non-linear stress-strain response, creep and fatigue. The fatigue based life prediction model developed accounts for the unusual ceramic behavior and also incorporates an experimentally determined oxide rate model. The model predicts the growth of this oxide scale to influence the intensity of the mechanic driving force, resulting from cyclic strains and stresses caused by thermally induced and externally imposed mechanical loads.

Demasi, Jeanine T.↗

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 NTRS2011

Reliability Evaluation of Base-Metal-Electrode (BME) Multilayer Ceramic Capacitors for Space Applications

This paper reports reliability evaluation of BME ceramic capacitors for possible high reliability space-level applications. The study is focused on the construction and microstructure of BME capacitors and their impacts on the capacitor life reliability. First, the examinations of the construction and microstructure of commercial-off-the-shelf (COTS) BME capacitors show great variance in dielectric layer thickness, even among BME capacitors with the same rated voltage. Compared to PME (precious-metal-electrode) capacitors, BME capacitors exhibit a denser and more uniform microstructure, with an average grain size between 0.3 and approximately 0.5 micrometers, which is much less than that of most PME capacitors. The primary reasons that a BME capacitor can be fabricated with more internal electrode layers and less dielectric layer thickness is that it has a fine-grained microstructure and does not shrink much during ceramic sintering. This results in the BME capacitors a very high volumetric efficiency. The reliability of BME and PME capacitors was investigated using highly accelerated life testing (HALT) and regular life testing as per MIL-PRF-123. Most BME capacitors were found to fail· with an early dielectric wearout, followed by a rapid wearout failure mode during the HALT test. When most of the early wearout failures were removed, BME capacitors exhibited a minimum mean time-to-failure of more than 10(exp 5) years. Dielectric thickness was found to be a critical parameter for the reliability of BME capacitors. The number of stacked grains in a dielectric layer appears to play a significant role in determining BME capacitor reliability. Although dielectric layer thickness varies for a given rated voltage in BME capacitors, the number of stacked grains is relatively consistent, typically between 10 and 20. This may suggest that the number of grains per dielectric layer is more critical than the thickness itself for determining the rated voltage and the life expectancy of the BME capacitor. Since BME capacitors have a much smaller grain size than PME capacitors, it is reasonable to predict that BME capacitors with thinner dielectric layers may have an equivalent life expectancy to that of PME capacitors with thicker dielectric layers.

Liu, David (Donghang)↗

NASA NTRS2024

Failure-Tolerant Avionics for Crewed Space Systems Recommended Best Practices

This paper provides an overview of some of the major steps needed to mature and justify the design of an avionics system for crewed spacecraft. It is organized as a collection of artifacts or pieces of evidence that NASA needs to assess the system at design reviews, including a functional failure modes and effects analysis (FFMEA), fault containment region (FCR) definitions, the failure hypothesis, and reliability analysis. This paper is intended as a reference for designers working on NASA crewed spaceflight projects, reliability engineers responsible for avionics system assessments, and program managers wanting to understand what evidence is required at design reviews to ensure crew safety and mission success.

Avionics↗

DOE OSTI2025

An Approach to Automate tools for the Risk Assessment of Digital Instrumentation and Control Systems

Reliable digital instrumentation and control systems (DI&C) are integral for sustaining the continued operation of nuclear power plants. These systems ensure that nuclear reactors operate safely, efficiently, and within regulatory requirements. Yet, the cost of designing and licensing new nuclear DI&C can be prohibitively expensive. Under the U.S. Department of Energy Light Water Reactor Sustainability Program, Idaho National Laboratory has developed a framework for supporting the risk-informed design of DI&C systems by offering methods to support the identification, quantification, and evaluation of risks for various DI&C design architectures. The framework indicates potential software failure modes and provides pathways for quantifying the potential for these software failures, including common cause failures. Using the framework’s systematic approach, challenges for assessing risks within new and existing nuclear DI&C systems can be reduced. Nevertheless, the current framework can be further improved using the convenience of automation. This paper introduces the development of Software for the Hazard Identification and Evaluation of Digital Systems (SHIELDS). SHIELDS is an engineering software package that enables the identification, elimination, and mitigation of potential risks and reduces the burden of deploying reliable DI&C systems. This work introduces plans and techniques to digitize and improve the manual risk assessment modules of the framework. These improvements will save time and increase the repeatability and usability of the framework, making it more accessible to a wider range of users. Ultimately, this introduces SHIELDS and how its modules support efficient development of safe and reliable DI&C systems.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2013

Electroluminescence and Transmission Electron Microscopy Characterization of Reverse-Biased AlGaN/GaN Devices

Reverse-bias stress testing has been applied to a large set of more than 50 AlGaN/GaN high electron mobility transistors, which were fabricated using the same process but with different values of the AlN mole fraction and the AlGaN barrier-layer thickness, as well as different substrates (SiC and sapphire). Two sets of devices having different defect types and densities, related to the different growth conditions and the choice of nucleation layer, were also compared. When subjected to gate drain (or gate-to-drain and source short-circuited) reverse-bias testing, all devices presented the same time-dependent failure mode, consisting of a significant increase in the gate leakage current. This failure mechanism occurred abruptly during step-stress experiments when a certain negative gate voltage, or critical voltage, was exceeded or, during constant voltage tests, at a certain time, defined as time to breakdown. Electroluminescence (EL) microscopy was systematically used to identify localized damaged areas that induced an increase of gate reverse current. This current increase was correlated with the increase of EL intensity, and significant EL emission during tests occurred only when the critical voltage was exceeded. Focused-ion-beam milling produced cross-sectional samples suitable for electron microscopy observation at the sites of failure points previously identified by EL microscopy. In highdefectivity devices, V-defects were identified that were associated with initially high gate leakage current and corresponding to EL spots already present in untreated devices. Conversely, identification of defects induced by reverse-bias testing proved to be extremely difficult, and only nanometer-size cracks or defect chains, extending vertically from the gate edges through the AlGaN/GaN heterojunction, were found. No signs of metal/semiconductor interdiffusion or extended defective areas were visible.

Cullen, David A↗

DOE OSTI2022

High-Temperature Linear Receiver Enabled by Multicomponent Aerogels

Concentrating solar thermal (CST) technology has significant potential mainly due to its dispatchability and low cost of storage. However, to compete with other sources, including utility-scale solar PV, its final cost (cents/kWh) still needs to be lowered. Cost reduction can be achieved by improving the system level efficiency of the CST plants through the deployment of advanced power cycles, which operate at high temperatures of ~700°C. However, optical, and thermal losses pose a major challenge to the efficiency of such CST systems. The overall aim of this project is to investigate and de-risk a linear solar receiver concept called an Aerogel Insulated Receiver (AIR) that generates high temperatures (up to 700°C) at a low solar concentration ratio (<100) and with a high collection efficiency (optical × receiver). Our prior work has demonstrated the thermal stability1 and optical and heat-insulating properties2,3 of transparent aerogel insulation at a one-inch scale. The focus of this work is on (1) co-optimization of the geometry of the aerogel tile and receiver enclosure to fit a standard parabolic collector (PTC), (2) scale-up of aerogels into 4-inch tiles while preserving key properties, (3) experimental measurement of receiver heat loss (W/m) in a >70-cm test stand and validation of anticipated receiver performance at high temperatures. Regarding (1), appropriate optical and thermal models for a parabolic trough receiver (PTR) are developed and validated. The geometry of the aerogels and the receiver enclosure are co-optimized to maximize the collection efficiency. The model predicts a 54% collection efficiency at 700°C for an AIR design based on flat aerogels. By combining the collection efficiency with the power block efficiency of supercritical CO 2 cycles, we predict >10% improvements in peak plant efficiency relative to existing line-focusing CST systems. The application of curved plasmonic aerogels is predicted to further increase the collection efficiency to 64%. Regarding (2), we demonstrate the successful development of 6-inch-long refractory aerogel tiles with optical, thermal, and stability characteristics consistent with our prior work. This scale-up requires a transition to a larger ALD station and modifying the ALD process variables such as exposure time and the number of precursor doses. Regarding (3), we design and develop an AIR test stand measuring 3 feet in length. Heat loss performance analysis is carried out using the test stand. The results indicate that aerogel insulation can significantly reduce receiver thermal losses at the high operating temperatures required for next-generation PTRs. The experimental results agree with the heat loss performance predicted by our receiver model. Lastly, we conducted preliminary failure mode and effects (FMEA) and techno-economic (TEA) analyses to identify failure mitigation strategies and commercial opportunities, respectively. Overall, this project identifies key opportunities and challenges in deploying aerogel insulating receivers in next-generation line-focusing CST technologies.

14 SOLAR ENERGY↗

DOE OSTI2022

Wind Turbine Drivetrain Reliability [Slides]

Pitch bearings, main bearings, and gearboxes in conventional wind turbine drivetrains often do not meet their 20-year minimum specified lifetime, resulting in turbine downtime as well as expensive, time-consuming repairs or replacements. The dominant failure modes of the drivetrain components and the conditions that lead to their failure are not fully accounted for during product design or routinely modeled for life management. Drivetrain reliability improvements and O&M cost reductions remain top priorities for both land-based and offshore wind turbines, especially as wind turbines continue to be deployed in increasingly remote and offshore locations, continue to increase in size, and are becoming expected to be in service beyond their original design life, all of which correspond to an increase in the impact of any reliability issues on O&M costs. This presentation summarizes the most recent activities by NREL and ANL on drivetrain reliability.

17 WIND ENERGY↗

DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗