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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Extended Bandwidth Spread Spectrum Time Domain Reflectometry Cable Test for Thermal Aging, Low Resistance Fault, and Water Detection

In 2022, researchers at Pacific Northwest National Laboratory (PNNL) used the Accelerated and Real-Time Environmental Nodal Assessment (ARENA) cable and motor test bed to characterize spread spectrum time domain reflectometry (SSTDR) and compare the responses of an SSTDR instrument to those of a frequency domain reflectometry (FDR) instrument. Results showed both techniques could detect and locate cable anomalies such as phase-to-phase low resistance and shorts, thermal insulation damage, mechanical insulation damage, and the presence or absence of water in some conditions. The SSTDR tests used a commercial instrument provided by LiveWire Innovations Inc. This commercial instrument performed tests at 6, 12, 24, and 48 MHz bandwidth. The results of these tests were compared to FDR tests where bandwidths could be extended up to 1.3 GHz, although the best responses for cable tests were from 100 to 500 MHz. Lower bandwidth signals can propagate better along the cable while higher bandwidths have higher resolution for impedance change reflections allowing more precise indication of location and separation of anomalies. The 2022 research found that FDR responses were clearer than SSTDR and speculated that a higher bandwidth SSTDR could more successfully detect and locate cable anomalies. One advantage of the SSTDR system investigated was that it was designed for energized online use up to 1,000 volts, which may be a significant advantage for nuclear power plant use. The LiveWire SSTDR instrument is an established product in the rail and aircraft industry and updating the SSTDR hardware parameters is difficult to justify without more conclusive testing. Therefore, a software adjustable laboratory SSTDR instrument was developed by PNNL and was used to test extended bandwidth SSTDR cable tests. Within the ARENA test bed, 42 cable conditions were tested with the PNNL SSTDR, FDR, and the LiveWire SSTDR—each operating at four different bandwidths. Observations and conclusions regarding the relative performance of the three instruments over different bandwidths are note below. Responses of the PNNL SSTDR (at 50 MHz) and the LiveWire SSTDR (at 48 MHz) were similar. The PNNL SSTDR higher frequency bandwidths behaved as expected showing sharper peaks and higher noise. This validated the PNNL SSTDR as a reasonable implementation of the SSTDR technology. Lower bandwidth SSTDR responses (particularly 6 and 12 MHz) may have increased value for use within longer cables but were not particularly effective at identifying anomalous cable behavior in the 100 ft cables tested here. The higher bandwidths of the PNNL SSTDR (50, 100, 200, and 400 MHz) did not provide substantially clearer cable reflectometry responses, but having the higher frequency responses available did add to the cable test evaluation. Strong responses to shorts and low impedance faults between phases were particularly evident in the higher bandwidth PNNL SSTDR and the FDR data. Measurements were repeatable, with similar responses obtained from a thermally aged cable for tests taken a month apart. Signal noise was affected in the unshielded cable by the local in-tray cable arrangement including proximity to metal edges and rungs of the cable tray. Foam isolation of the cable from the tray metal reduced in both FDR and SSTDR responses. Cable condition monitoring in nuclear power plants will likely benefit from both more informative off-line testing methods and from the development of on-line methods for continuous monitoring of cables in use. The LWRS-funded ARENA test bed was a valuable resource for this development and direct comparison of nuclear electrical cable condition monitoring technologies. Test results are targeted to guide industry advancement of testing and monitoring tools for cable aging management.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1980

Rectenna array measurement results

The measured performance characteristics of a rectenna array are reviewed and compared to the performance of a single element. It is shown that the performance may be extrapolated from the individual element to that of the collection of elements. Techniques for current and voltage combining are demonstrated. The array performance as a function of various operating parameters is characterized and techniques for overvoltage protection and automatic fault clearing in the array are demonstrated. A method for detecting failed elements also exists. Instrumentation for deriving performance effectiveness is described. Measured harmonic radiation patterns and fundamental frequency scattered patterns for a low level illumination rectenna array are presented.

Dickinson, R. M.↗

NASA NTRS2020

In-Flight Detection of Vibration Anomalies in Unmanned Aerial Vehicles

Owing to the frequency of occurrence and high risk associated with bearings, identification and characterization of bearing faults in motors via nondestructive evaluation (NDE) methods have been studied extensively, amongst which vibration analysis has been found to be a promising technique for early diagnosis of anomalies. However, a majority of the existing techniques rely on vibration sensors attached onto or in close proximity to the motor in order to collect signals with a relatively high SNR. Due to weight and space restrictions, these techniques cannot be used in unmanned aerial vehicles (UAVs), especially during flight operations since accelerometers cannot be attached onto motors in small UAVs. Small UAVs are often subjected to vibrational disturbances caused by multiple factors such as weather turbulence, propeller imbalance or bearing faults. Such anomalies may not only pose risks to UAV's internal circuitry, components or payload, they may also generate undesirable noise level particularly for UAVs expected to fly in low-altitudes or urban canyon. This paper presents a detailed discussion of challenges in in-flight detection of bearing failure in UAVs using existing approaches and offers potential solutions to detect overall vibration anomalies in small UAV operations based on IMU data.

Portia Banerjee↗

DOE OSTI2024

Intermittent Criticality Multi‐Scale Processes Leading to Large Slip Events on Rough Laboratory Faults

Abstract We discuss data of three laboratory stick‐slip experiments on Westerly Granite samples performed at elevated confining pressure and constant displacement rate on rough fracture surfaces. The experiments produced complex slip patterns including fast and slow ruptures with large and small fault slips, as well as failure events on the fault surface producing acoustic emission bursts without externally‐detectable stress drop. Preparatory processes leading to large slips were tracked with an ensemble of ten seismo‐mechanical and statistical parameters characterizing local and global damage and stress evolution, localization and clustering processes, as well as event interactions. We decompose complex spatio‐temporal trends in the lab‐quake characteristics and identify persistent effects of evolving fault roughness and damage at different length scales, and local stress evolution approaching large events. The observed trends highlight labquake localization processes on different spatial and temporal scales. The preparatory process of large slip events includes smaller events marked by confined bursts of acoustic emission activity that collectively prepare the fault surface for a system‐wide failure by conditioning the large‐scale stress field. Our results are consistent overall with an evolving process of intermittent criticality leading to large failure events, and may contribute to improved forecasting of large natural earthquakes.

Geochemistry & Geophysics↗

DOE OSTI2021

Determining diagnostic coverage for memory using redundant execution

Memory, used by a computer to store data, is generally prone to faults, including permanent faults (i.e. relating to a lifetime of the memory hardware), and also transient faults (i.e. relating to some external cause) which are otherwise known as soft errors. Since soft errors can change the state of the data in the memory and thus cause errors in applications reading and processing the data, there is a desire to characterize the degree of vulnerability of the memory to soft errors. In particular, once the vulnerability for a particular memory to soft errors has been characterized, cost/reliability trade-offs can be determined, or soft error detection mechanisms (e.g. parity) may be selectively employed for the memory. In some cases, memory faults can be diagnosed by redundant execution and a diagnostic coverage may be determined.

Bramley, Richard Gavin↗

DOE OSTI2023

Liveness as a factor to evaluate memory vulnerability to soft errors

Bramley, Richard Gavin↗

NASA NTRSDate not supplied

Firmware Architecture of the ARMADAS Bolting Robot

The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project, under development at NASA Ames Research Center, has demonstrated on-ground autonomous robotic assembly of extensive digital structures, and it is now moving forward towards in-space demonstration. The ARMADAS system comprises of the operation software, the operation user interface (opsUI), and a swarm of robots. The robotic system consists of a multitude of collaborative agents specifically designed to transport, place and bolt the building blocks, called voxels (volumetric pixels). This paper focuses on the bolting robot, referred to as Mobile Metamaterial Internal Co-Integrator (MMIC-I). MMIC-I is a battery-powered crawling robot. It navigates the structure through extension, contraction and gripping. Two distinct controller boards operate the robot's two symmetric modules, referred to as module A and B. Board A is the master board: it coordinates motion planning and motion primitives execution, hosts the WiFi client, performs periodic self-assessment and system idle check and triggers faults if anomalies are detected. Board B periodically sends a heartbeat to board A, through a wired communication channel that uses the Serial protocol. Additionally, board A's WiFi client receives heartbeat packet requests or motion/bolting commands from a dedicated server board, and acknowledges reception sending back a response heartbeat packet containing information about the overall robot status, e. g. electrical current and voltage values, target and actual angles, operating mode, fault status. Whenever a motion command is sent, the motion planning section of the firmware determines the current robot configuration, using Inertial Measurement Unit readings and the motors Pulse Width Modulation values. Afterwards, it calculates the list of primitives needed to reach the target state, and controls their execution in the proper order. MMIC-I can receive and execute motion and bolting commands only when it is in operational mode. MMIC-I has three operating modes: standby, operational and safed. Standby mode is automatically entered upon startup. While in standby mode, all motors are powered off, and the only accepted commands are the ones relative to a change of mode and heartbeat packet request. Fault detection causes the robot to automatically enter safed or standby mode. Whenever the detected fault occurs within a motion and requires immediate intervention, e. g. an over-current situation, the robot enters safed mode. Safed mode powers off all motors except for the locomotion module, thus preventing the robot from collapsing. Conversely, when the detected fault doesn't require immediate intervention (low battery warning, for instance), the robot enters standby mode after completing the ongoing motion. This paper provides a detailed discussion of MMIC-I's firmware architecture. It accurately describes the implementation approach for each module: sensor data reading, motor control and actuation, WiFi server-client communication, intra-boards Serial communication, operating modes and autonomous fault detection, motion planning, coordination and execution, etc. Moreover, in support of the software description, this paper includes a thorough characterization of MMIC-I's hardware and avionics.

In-space assembly↗

DOE OSTI2021

SIR Glass Test Vehicle Designed to Characterize Process Materials

The exposure to harsh environments can result in reliability issues on highly dense electronic packages and circuit assemblies. One area of concern is electrochemical failure mechanisms caused by electrolytic corrosion, electrochemical migration (dendrites), and leakage currents under humid conditions. These no-fault and no-trouble found failure points are challenging to detect and to perform process control during the assembly process. The focus of this research centers on metallizing glass surface insulation resistance test vehicles to characterize materials on electronic packages and components that exhibit the highest risk of failure due to material interactions and process residues. Glass test vehicles provide a visual representation of materials when they are exposed to moisture content in the air. Harsh environments can cause material interactions resulting in morphology changes, opening, and release of active components. A transparent test vehicle is highly functional for environmental stress testing under temperature, humidity, and bias. The designed experiment will evaluate the effects of material and process residues that can undergo changes in behavior during application, which leads to local failures.

Capen, Bill↗

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.↗

DOE OSTI2020

Data analytics for leak detection in a subcritical boiler

For decades, boiler leaks have been the leading cause of forced outages in the coal-fired unit. The leak occurrences are currently escalating since the existing plants must satisfy faster-ramping rates to support grid operation. Data analytics including Principal Component Analysis, Canonical Variate, and Fisher Discriminant Analysis were combined for detecting and characterizing the leak in a commercial 650 MW subcritical coal-fired power plant. The combined approach was shown to be highly effective in the fault investigation that would not have been easily achieved by an individual technique. The variability in both training and validation datasets was first evaluated using PCA. Then, the CV-FDA was employed to discriminate among faults, and to categorize the processed data into two main groups: no-leak (0) and leak (1), providing the timeframe and location of the leak occurrence. Furthermore, about 8,014 observations from 81 process variables were initially included in the calculation, while the variable count was reduced to 4 with less than 1% misclassification rate in total observations. Finally, the leak was isolated in the waterwall section. Thus, the outcome of this research may provide early detection and isolation of faulty operations in the coal-fired power plant that involves a considerable number of process variables.

20 FOSSIL-FUELED POWER PLANTS↗

NASA NTRS2000

Software Fault Tolerance: A Tutorial

Because of our present inability to produce error-free software, software fault tolerance is and will continue to be an important consideration in software systems. The root cause of software design errors is the complexity of the systems. Compounding the problems in building correct software is the difficulty in assessing the correctness of software for highly complex systems. After a brief overview of the software development processes, we note how hard-to-detect design faults are likely to be introduced during development and how software faults tend to be state-dependent and activated by particular input sequences. Although component reliability is an important quality measure for system level analysis, software reliability is hard to characterize and the use of post-verification reliability estimates remains a controversial issue. For some applications software safety is more important than reliability, and fault tolerance techniques used in those applications are aimed at preventing catastrophes. Single version software fault tolerance techniques discussed include system structuring and closure, atomic actions, inline fault detection, exception handling, and others. Multiversion techniques are based on the assumption that software built differently should fail differently and thus, if one of the redundant versions fails, it is expected that at least one of the other versions will provide an acceptable output. Recovery blocks, N-version programming, and other multiversion techniques are reviewed.

Torres-Pomales, Wilfredo↗

NASA NTRS1983

Algorithm development for a spacecraft autonomous power system

Two algorithms were developed to demonstrate the implementation of autonomous functions in an existing spacecraft power system. The functions selected for autonomous operation include a typical performance monitoring function, battery state of charge, and a fault detection and response function represented by a battery state of charge below a preselected limit. The constraints imposed by the existing power system configuration are the available data in the telemetry stream and the existing commands and command structure. The areas requiring future development are the degree of battery characterization, the effects of hardware/software faults, and the verification of faults.

Bridgeforth, A. O.↗

DOE OSTI2021

A propagation-based fault detection and discrimination method and the optimization of sensor deployment

Industrial processes can be affected by faults having a serious impact on operation when not promptly detected and diagnosed. Here in this paper, a propagation-based fault detection and discrimination(PFDD) method is proposed to develop a strategy for fault diagnosis while in the design phase of a system. The PFDD method constructs the system model using the Integrated System Fault Analysis(ISFA) technique. Based on the system model, the propagation of hardware and software faults are simulated qualitatively. Given the results of the simulation, the process by which a fault propagates can be characterized using the qualitative features of system variables including the deviation of the system variables from their expected values, the variation of the system variables over time, and the order in which each variable is influenced during the propagation of the fault. The strategy by which a fault can be detected and discriminated is defined using those features. The PFDD method supports the detection and discrimination of faults in both steady states and transient states. Based on the PFDD method, the optimization of sensor deployment in a system is discussed. A brute force algorithm is developed to examine the system’s capability at diagnosing faults and the cost of sensor deployment for all possible configurations of sensors. The optimal sensor deployment strategy can be derived accordingly. However, the brute force method is only applicable to small-scale systems due to its high computational cost. A genetic algorithm is used to optimize sensor deployment in large-scale systems. The PFDD and sensor deployment optimization methods are applied to the Experimental Breeder Reactor II (EBR-II) for verification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS1987

Talemzane - Algerian impact crater detected on SIR-A orbital imaging radar

In November, 1981, NASA's first Shuttle Imaging Radar mission (SIR-A) began producing maplike photographic strips of Earth scenes from orbital altitude. A Saharan radar image acquired over Algeria clearly delineates two sedimentary basins, Erg Occidental and Erg Oriental, separated by an elongated zone of exposed bedrock, the M'Zab Chebka. At the NE margin of the Chebka, rimrocks, slopes, and ejecta deposits of Talemzane meteorite impact crater appear as a distinct two km wide radar-bright ring. This unique circle of strong radar backscatter distinguishes the solitary impact structure from numerous dayas (similarly appearing karstic depressions) which characterize the region. The crater is prominent on radar, but is obscure on optically obtained satellite and aircraft images, as are partly buried fluvial drainage systems and fault-block traces developed in bedrocks of the Chebka. Radar detection of an annular drainage system indicates possible presence of a ring graben at the crater. Brightest radar signals on the image are cultural features at recently developed gas fields near Hassi er R'Mel.

Mchone, John F.↗

DOE OSTI2020

Automatic Fault Detection & Diagnostics: Residential Market Analysis

This report provides an overview of the market potential for automated fault detection and diagnostics (AFDD) in the residential sector, focusing on embedded AFDD in central air conditioners (CAC) and air-source heat pumps (ASHP) as well as the use of smart diagnostic tools to ensure quality installation of CAC/ASHP equipment. The contents include background on AFDD, stakeholder engagement efforts, a technology assessment of AFDD and smart diagnostic tools, residential CAC and ASHP market trends, a characterization of the CAC and ASHP installed base, a synopsis of utility provider programs, and market barriers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2021

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects

Researchers at New Mexico Tech, Los Alamos National Laboratory (LANL), Sandia National Laboratory (SNL), and other collaborators are developing a methodology to improve characterization of stress in the subsurface by means of a model-based inversion of reservoir engineering data, time lapse seismic measurements, and microseismicity. Historically, various geophysical techniques have been used in efforts to understand the state of stress in the subsurface through direct and indirect imaging of stress sensitive features (faults and fractures) and observations of transient stress related observations (time variant elastic moduli and microseismicity). Standard direct and indirect techniques for seismic fault and fracture imaging suffer from detectability limits due to reliance on high data quality and multiplicity. Variations in effective elastic properties from inversion of high quality seismic (Vertical Seismic Profile (VSP)) data may be used to infer stress near the wellbore through integration with independent experimental characterization of the stress-velocity relationship. Given a sufficiently robust observation network, microseismic emissions may be inverted to characterize focal mechanisms which, together with supporting assumptions and constraints, inform estimates of in-situ stress. While each of these techniques contributes in part to the characterization of stress within limited spatial and temporal domains, no one method provides an unambiguous stress measurement or a predictive capability over a site scale spatial extent. Challenges associated with solution non-uniqueness, measurement ambiguity, and irregular sampling may be greatly minimized through combination of one or more of independent measurements within a common framework in which realistic geological, hydrodynamic, geomechanical, and seismological constituent process models may act as constraints.

02 PETROLEUM↗

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↗

NASA NTRS1989

Predeployment validation of fault-tolerant systems through software-implemented fault insertion

Fault injection-based automated testing (FIAT) environment, which can be used to experimentally characterize and evaluate distributed realtime systems under fault-free and faulted conditions is described. A survey is presented of validation methodologies. The need for fault insertion based on validation methodologies is demonstrated. The origins and models of faults, and motivation for the FIAT concept are reviewed. FIAT employs a validation methodology which builds confidence in the system through first providing a baseline of fault-free performance data and then characterizing the behavior of the system with faults present. Fault insertion is accomplished through software and allows faults or the manifestation of faults to be inserted by either seeding faults into memory or triggering error detection mechanisms. FIAT is capable of emulating a variety of fault-tolerant strategies and architectures, can monitor system activity, and can automatically orchestrate experiments involving insertion of faults. There is a common system interface which allows ease of use to decrease experiment development and run time. Fault models chosen for experiments on FIAT have generated system responses which parallel those observed in real systems under faulty conditions. These capabilities are shown by two example experiments each using a different fault-tolerance strategy.

Czeck, Edward W.↗