SEARCH · Engineering Papers

Results for “faulting”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Real-Time Cavity Fault Prediction in CEBAF Using Deep Learning

Data-driven prediction of future faults is a major research area for many industrial applications. In this work, we present a new procedure of real-time fault prediction for superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF) using deep learning. CEBAF has been afflicted by frequent downtime caused by SRF cavity faults. We perform fault prediction using pre-fault RF signals from C100-type cryomodules. Using the pre-fault signal information, the new algorithm predicts the type of cavity fault before the actual onset. The early prediction may enable potential mitigation strategies to prevent the fault. In our work, we apply a two-stage fault prediction pipeline. In the first stage, a model distinguishes between faulty and normal signals using a U-Net deep learning architecture. In the second stage of the network, signals flagged as faulty by the first model are classified into one of seven fault types based on learned signatures in the data. Initial results show that our model can successfully predict most fault types 200 ms before onset. We will discuss reasons for poor model performance on specific fault types.

Rahman, M.↗

DOE OSTI2017

Fault detection in PV systems

A practical fault detection approach for PV systems intended for online implementation is developed. The fault detection model here is built using artificial neural network. initially the photovoltaic system is simulated using MATLAB software and output power is collected for various combinations of irradiance and temperature. Data is first collected for normal operating condition and then four types of faults are simulated and data are collected for faulty conditions. Four faults are considered here and they are: Line to Line faults with a small voltage difference, Line to line faults with a large voltage difference, degradation fault and open-circuit fault. This data is then used to train the neural network and to develop the fault detection model. The fault detection model takes irradiance, temperature and power as the input and accurately gives the type of fault in the PV system as the output. This system is a generalized one as any PV module datasheet can be used to simulate the Photovoltaic system and also this fault detection system can be implemented online with the use of data acquisition system.

14 SOLAR ENERGY↗

NASA NTRS1975

Faults on Skylab imagery of the Salton Trough area, Southern California

The author has identified the following significant results. Large segments of the major high angle faults in the Salton Trough area are readily identifiable in Skylab images. Along active faults, distinctive topographic features such as scarps and offset drainage, and vegetation differences due to ground water blockage in alluvium are visible. Other fault-controlled features along inactive as well as active faults visible in Skylab photography include straight mountain fronts, linear valleys, and lithologic differences producing contrasting tone, color or texture. A northwestern extension of a fault in the San Andreas set, is postulated by the regional alignment of possible fault-controlled features. The suspected fault is covered by Holocene deposits, principally windblown sand. A northwest trending tonal change in cultivated fields across Mexicali Valley is visible on Skylab photos. Surface evidence for faulting was not observed; however, the linear may be caused by differences in soil conditions along an extension of a segment of the San Jacinto fault zone. No evidence of faulting could be found along linears which appear as possible extensions of the Substation and Victory Pass faults, demonstrating that the interpretation of linears as faults in small scale photography must be corroborated by field investigations.

Merifield, P. M.↗

NASA NTRS1986

Measurement and application of fault latency

The time interval between the occurrence of a fault and the detection of the error caused by the fault is divided by the generation of that error into two parts: fault latency and error latency. Since the moment of error generation is not directly observable, all related works in the literature have dealt with only the sum of fault and error latencies, thereby making the analysis of their separate effects impossible. To remedy this deficiency, (1) a new methodology for indirectly measuring fault latency is presented; the distribution of fault latency is derived from the methodology; and (3) the knowledge of fault latency is applied to the analysis of two important examples. The proposed methodology has been implemented for measuring fault latency in the Fault-Tolerant Multiprocessor (FTMP) at the NASA Airlab. The experimental results show wide variations in the mean fault latencies of different function circuits within FTMP. Also, the measured distributions of fault latency are shown to have monotone hazard rates. Consequently, Gamma and Weibull distributions are selected for the least-squares fit as the distribution of fault latency.

Shin, K. G.↗

NASA NTRS1990

Fault diagnosis

The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

Abbott, Kathy↗

NASA NTRS1992

Advanced information processing system: Fault injection study and results

The objective of the AIPS program is to achieve a validated fault tolerant distributed computer system. The goals of the AIPS fault injection study were: (1) to present the fault injection study components addressing the AIPS validation objective; (2) to obtain feedback for fault removal from the design implementation; (3) to obtain statistical data regarding fault detection, isolation, and reconfiguration responses; and (4) to obtain data regarding the effects of faults on system performance. The parameters are described that must be varied to create a comprehensive set of fault injection tests, the subset of test cases selected, the test case measurements, and the test case execution. Both pin level hardware faults using a hardware fault injector and software injected memory mutations were used to test the system. An overview is provided of the hardware fault injector and the associated software used to carry out the experiments. Detailed specifications are given of fault and test results for the I/O Network and the AIPS Fault Tolerant Processor, respectively. The results are summarized and conclusions are given.

Burkhardt, Laura F.↗

NASA NTRS1994

The Teton fault, Wyoming: Topographic signature, neotectonics, and mechanisms of deformation

We integrated geophysical and geological methods to evalute the structural evolution of the active Teton normal fault, Wyoming, and its role in the development of the dramatic topography of Teton Range and Jackson Hole. Comparison of variations in surface offsets with the topographic expression of the Teton range crest and drainage divide, and the overall structure of the range, suggests that the effects ofpostglacial faulting cannot be discriminated from the influence of pre-extensional structures and differential; erosion on the footwall topography. In contrast, the effects of multiple scarp-forming normal faulting earthquakes are expressed by the anomalous drainage pattern and westward tilt of the hanging wall, Jackson Hole, toward the Teton fault. Kinematic boundary element fault models suggest that the westward tilt of the valley floor is the product of 110-125 m of displacement on a 45 deg-75 deg E dipping Teton fault in the past 25,000-75,000 years. Comparisons with historic normal faulting earthquake displacements imply that this range of displacement corresponds to 10-50, M greater than 7 scarp-forming earthquakes. A total throw of 2.5 to 3.5 km across the Teton fault is suggested by inverse ray-tracing and forward gravity models. These models also suggest that Laramide age structures have been offset across the Teton fault and obscure its geophysical signature but also continue to influence the structural and topographic expression of the footwall and hanging wall blocks. Paleomagnetic analyses of the approximately 2.0 Ma Huckelberry Ridge Tuff suggest that the overall westward tilt of the Teton Range is a result ofabout 10 deg of west side down tilt across the Teton fault since tuff emplacement. This suggests that much if not all of the throw across the Teton fault has accumulated in the past 2 m.y. Complex demagnetization and rock magnetic behavior and local emplacement of the Huckleberry Ridge Tuff on preexisting topogrpahy preclude determination of the amount or variations in throw along strike of the Teton fault from the paleomagnetic data.

Byrd, John O. D.↗

NASA NTRS2005

Geometry of Thrust Faults Beneath Amenthes Rupes, Mars

Amenthes Rupes is a 380 km-long lobate fault scarp located in the eastern hemisphere of Mars near the dichotomy boundary. The scarp is marked by about 1 km of vertical separation across a northeast dipping thrust fault (top to the SW) and offsets heavily-cratered terrain of Late Noachian age, the visible portion of which was in place by 3.92 Ga and the buried portion in place between 4.08 and 4.27 Ga. The timing of scarp formation is difficult to closely constrain. Previous geologic mapping shows that near the northern end of Amenthes Rupes, Hesperian age basalts terminate at the scarp, suggesting that fault slip predated the emplacement of these flows at 3.69 to 3.9 Ga. Maxwell and McGill also suggest the faulting ceased before the final emplacement of the Late Hesperian lavas on Isidis Planitia. The trend of the faults at Amenthes, like many thrust faults at the dichotomy boundary, parallels the boundary itself. Schultz and Watters used a dislocation modeling program to match surface topography and vertical offset of the scarp at Amenthes Rupes, varying the dip and depth of faulting, assuming a slip of 1.5 km on the fault. They modeled faulting below Amenthes Rupes as having a dip of between 25 and 30 degrees and a depth of 25 to 35 km, based on the best match to topography. Assuming a 25 degree dip and surface measurements of vertical offset of between 0.3 and 1.2 km, Watters later estimated the maximum displacement on the Amenthes Rupes fault to be 2.90 km. However, these studies did not determine the geometry of the thrust using quantitative constraints that included shortening estimates. Amenthes Rupes deforms large preexisting impact craters. We use these craters to constrain shortening across the scarp and combine this with vertical separation to infer fault geometry. Fault dip was also estimated using measurements of scarp morphology. Measurements were based on 460 m (1/128 per pixel) digital elevation data from the Mars Orbiter Laser Altimeter (MOLA), an instrument on the Mars Global Surveyor (MGS) satellite.

Vidal, A.↗

NASA NTRS2011

Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme

A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred

Tang, Liang↗

DOE OSTI2020

An Overview of Spread Spectrum Time Domain Reflectometry Responses to Photovoltaic Faults

Spread spectrum time domain reflectometry (SSTDR) is a broadband electrical reflectometry technique that has been used to detect and locate faults on live electrical systems, including photovoltaic systems. In this article, we evaluate the detectability and localizability from both existing literature and our own measurements using SSTDR of open-circuit faults, connection faults, short-circuit faults, ground faults, arc faults, shading faults, bypass diode faults, and accelerated degradation faults in PV cells and mini-modules. Additionally, reflection magnitudes for these faults are compared. Herein, preliminary data on buried and grounded PV cable along with arc fault detection are presented.

14 SOLAR ENERGY↗

DOE OSTI2023

1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

This paper presents a 1-D convolutional and graph convolutional networks for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined 1-D convolutional and graph convolutional networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The accuracy of 99.5%, 98.4%, 99.2%, and 95.5% are achieved in fault event detection, fault type classification, fault phase identification, and fault location respectively. The detailed confusion matrices of fault type and fault phase classification are provided for validation.

deep neural network↗

NASA NTRS2012

Improving Multiple Fault Diagnosability using Possible Conflicts

Multiple fault diagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.

multiple faults↗

DOE OSTI2020

Automated fault detection of residential air-conditioning systems using thermostat drive cycles

Residential air conditioning equipment comprises a significant portion of the total energy consumption of a home. Unfortunately, air-conditioning systems can be susceptible to faulty operation either from installation errors or faults that accrue over the equipment’s lifetime. This paper presents a novel automated fault detection algorithm for residential air-conditioning systems that can alert the homeowner of the presence of these faults. The proposed algorithm utilizes only the home’s thermostat and outside air temperature to perform automated fault detection over the course of the equipment’s lifetime, including immediately after installation. The algorithm uses an extended Kalman filter approach to identify a three-resistor, two-capacitor (3R2C) electrical equivalent thermodynamic model. The identified 3R2C model is used to predict cooling times during a testing period comprising of a series of thermostat drive-cycle experiments. We tested the algorithm on an EnergyPlus™ model of a typical residential building in Orlando, Florida. Duct faults, indoor airflow faults, and refrigerant undercharge faults were introduced into the building model one at a time. The algorithm was able to accurately determine duct-leak faults, 40% airflow faults, 40% undercharge faults, and no-fault cases with an accuracy of 70%, 77%, 82%, and 87%, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Dynamic simulation of CO 2 -injection-induced fault rupture with slip-rate dependent friction coefficient

It is known that poro-elastic stress and effective stress reduction associated with deep underground fluid injection can potentially trigger shear rupture along pre-existing faults. We modeled an idealized CO 2 injection scenario, to assess the effects on faults in the first phase of a generic CO 2 aquifer storage operation. We used coupled multiphase fluid flow and geomechanical numerical modeling to evaluate the stress and pressure perturbations induced by fluid injection and the response of a nearby normal fault. Slip-rate dependent friction and inertial effects have been taken into account during rupture. Contact elements have been used to take into account the frictional behavior of the rupture plane. We investigated different scenarios of injection rate to induce rupture on the fault, employing various fault rheologies. Published laboratory data on CO 2 -saturated intact and crushed rock samples, representative of a potential target aquifer, sealing formation and fault gouge, have been used to define a scenario where different fault rheologies apply at different depths. Nucleation of fault rupture takes place at the bottom of the reservoir, in agreement with analytical poro-elastic stress calculations, depending on injection-induced reservoir inflation and the tectonic stress scenario. For the stress state considered here, the first triggered rupture always produces the largest rupture length and slip magnitude, both of which correlate with the fault rheology. Velocity weakening produces larger ruptures and generates larger magnitude seismic events. Heterogeneous faults have been considered including velocity-weakening or velocity strengthening sections inside and below the aquifer, with the upper sections being velocity-neutral. Nucleation of rupture in a velocity-strengthening section results in a limited rupture extension, both in terms of maximum slip and rupture length. For a heterogeneous fault with nucleation in a velocity-weakening section, the rupture may propagate into the overlying velocity-neutral section, if the extent of velocity-weakening and associated friction drop are large enough.

58 GEOSCIENCES↗

DOE OSTI2021

Fault Detection on Seismic Structural Images Using a Nested Residual U-Net

Automatic identification of faults on seismic structural images is a challenging yet crucial task in quantitative seismic interpretation. Human picking or attribute-based fault detection methods may misidentify faults on noisy, complex seismic images. In this work, we develop a new automatic fault detection method using a nested residual U-shaped convolutional neural network. Each of the encoders and decoders in this neural network is a residual U-Net, leading to a nested architecture. The final fault map results from the fusion of three fault maps with low, medium, and high fault resolutions. We demonstrate the excellent fault-detection capability of our nested neural network using a series of synthetic and field seismic images. We find that our approach produces clearer and more interpretable fault maps than the current state-of-the-art U-Net fault detection method, particularly on noisy seismic images. Our new automatic fault detection method can facilitate reliable quantitative seismic interpretation on field seismic images.

58 GEOSCIENCES↗

DOE OSTI2022

Barriers to Broader Utilization of Fault Detection Technologies for Improving Residential HVAC Equipment Efficiency

Faults in residential heating, ventilating, and air conditioning (HVAC) equipment may occur due to poor installation practices or develop over time, and these faults can negatively impact system efficiency, thermal comfort, and equipment lifespan. Automated fault detection and diagnostic (AFDD) technologies identify energy wasting HVAC faults, such as low indoor airflow and improper refrigerant charge, and guide technicians in improving system efficiency. For residential HVAC, AFDD consists of a range of fault detecting and diagnostic capabilities, sensor configurations, and target applications. AFDD technology can either be permanently installed by the original equipment manufacturer (OEM) using embedded sensors or as an add-on product either during or after installation. Additionally, several advanced installation tools and refrigerant gauge sets include AFDD features for temporary use during equipment installation and tune-ups. Some technologies can detect a fault but have limited diagnostic capabilities. For example, a single-point measurement from the home's thermostat or energy monitor can provide certain fault detection capability by analyzing the equipment runtime or energy consumption. These technologies, though limited at determining the cause of a given fault, may have significant energy savings potential due to their low cost and prevalence in the residential HVAC market. Despite the potential benefits, fault detection technologies face many technical and market barriers preventing broad adoption. Beyond the cost barriers due to the added sensor requirements and technology development, fault detection technologies face many implementation and adoption barriers such as installer training, customer awareness, standardized communication protocols, and methods of test for evaluating accuracy. The purpose of this whitepaper is to characterize market and technical barriers impeding broader utilization of fault detection technology for residential HVAC energy efficiency applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2014

Development of Asset Fault Signatures for Prognostic and Health Management in the Nuclear Industry

Proactive online monitoring in the nuclear industry is being explored using the Electric Power Research Institute’s Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. The FW-PHM Suite is a set of web-based diagnostic and prognostic tools and databases that serves as an integrated health monitoring architecture. The FW-PHM Suite has four main modules: Diagnostic Advisor, Asset Fault Signature (AFS) Database, Remaining Useful Life Advisor, and Remaining Useful Life Database. This paper focuses on development of asset fault signatures to assess the health status of generator step-up generators and emergency diesel generators in nuclear power plants. Asset fault signatures describe the distinctive features based on technical examinations that can be used to detect a specific fault type. At the most basic level, fault signatures are comprised of an asset type, a fault type, and a set of one or more fault features (symptoms) that are indicative of the specified fault. The AFS Database is populated with asset fault signatures via a content development exercise that is based on the results of intensive technical research and on the knowledge and experience of technical experts. The developed fault signatures capture this knowledge and implement it in a standardized approach, thereby streamlining the diagnostic and prognostic process. This will support the automation of proactive online monitoring techniques in nuclear power plants to diagnose incipient faults, perform proactive maintenance, and estimate the remaining useful life of assets.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗