SEARCH · Engineering Papers

Results for “failure detection”

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 1,027 records · Page 57

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Leveraging Community and Author Context to Explain the Performance and Bias of Text-Based Deception Detection Models

Deceptive news posts shared in online communities can be detected with NLP models, and much recent research has focused on the development of such models. In this work, we use characteristics of online communities and authors --- the context of how and where content is posted --- to explain the performance of a neural network deception detection model and identify sub-populations who are disproportionately affected by model accuracy or failure. We examine who is posting the content, and where the content is posted to. We find that while author characteristics are better predictors of deceptive content than community characteristics, both characteristics are strongly correlated with model performance. Traditional performance metrics such as F1 score may fail to capture poor model performance on isolated sub-populations such as specific authors, and as such, more nuanced evaluation of deception detection models is critical.

machine learning (ML), machine learning explanatio↗

DOE OSTI2022

Separating Sensor Anomalies From Process Anomalies in Data-Driven Anomaly Detection

Data-driven anomaly detection over time series data is studied from the perspective of separating data anomalies—corresponding to sensor failures—from process anomalies—that arise from equipment or operational failures. Herein, a semi-supervised approach is proposed that utilizes two predictive models trained on non-anomalous data using two different sensor groups as inputs, and a nested hypothesis test to reliably classify data or process anomalies. Conditions are derived on choice of sensor groups to guarantee reliable detection, and a case study is presented to demonstrate the proposed classification approach.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2012

Fiber Bragg Grating Sensor System for Monitoring Smart Composite Aerospace Structures

Lightweight, electromagnetic interference (EMI) immune, fiber-optic, sensor- based structural health monitoring (SHM) will play an increasing role in aerospace structures ranging from aircraft wings to jet engine vanes. Fiber Bragg Grating (FBG) sensors for SHM include advanced signal processing, system and damage identification, and location and quantification algorithms. Potentially, the solution could be developed into an autonomous onboard system to inspect and perform non-destructive evaluation and SHM. A novel method has been developed to massively multiplex FBG sensors, supported by a parallel processing interrogator, which enables high sampling rates combined with highly distributed sensing (up to 96 sensors per system). The interrogation system comprises several subsystems. A broadband optical source subsystem (BOSS) and routing and interface module (RIM) send light from the interrogation system to a composite embedded FBG sensor matrix, which returns measurand-dependent wavelengths back to the interrogation system for measurement with subpicometer resolution. In particular, the returned wavelengths are channeled by the RIM to a photonic signal processing subsystem based on powerful optical chips, then passed through an optoelectronic interface to an analog post-detection electronics subsystem, digital post-detection electronics subsystem, and finally via a data interface to a computer. A range of composite structures has been fabricated with FBGs embedded. Stress tensile, bending, and dynamic strain tests were performed. The experimental work proved that the FBG sensors have a good level of accuracy in measuring the static response of the tested composite coupons (down to submicrostrain levels), the capability to detect and monitor dynamic loads, and the ability to detect defects in composites by a variety of methods including monitoring the decay time under different dynamic loading conditions. In addition to quasi-static and dynamic load monitoring, the system can capture acoustic emission events that can be a prelude to structural failure, as well as piezoactuator-induced ultrasonic Lamb-waves-based techniques as a basis for damage detection.

Moslehi, Behzad↗

NASA NTRS1988

Fatigue testing and damage development in continuous fiber reinforced metal matrix composites

A general overview of the fatigue behavior of metal matrix composites (MMC) is presented. The first objective is to present experimental procedures and techniques for conducting a meaningful fatigue test to detect and quantify fatigue damage in MMC. These techniques include interpretation of stress-strain responses, acid etching of the matrix, edge replicas of the specimen under load, radiography, and micrographs of the failure surfaces. In addition, the paper will show how stiffness loss in continuous fiber reinforced metal matrix composites can be a useful parameter for detecting fatigue damage initiation and accumulation. Second, numerous examples of how fatigue damage can initiate and grow in various MMC are given. Depending on the relative fatigue behavior of the fiber and matrix, and the interface properties, the failure modes of MMC can be grouped into four categories: (1) matrix dominated, (2) fiber dominated, (3) self-similar damage growth, and (4) fiber/matrix interfacial failures. These four types of damage will be discussed and illustrated by examples with the emphasis on the fatigue of unnotched laminates.

Johnson, W. S.↗

NASA NTRS1989

Fatigue testing and damage development in continuous fiber reinforced metal matrix composites

Johnson, W. S.↗

DOE OSTI2022

Subsurface stress criticality associated with fluid injection and determined using machine learning

Machine-learning methods and apparatus are disclosed to determine critical state or other parameters related to fluid-driven failure of a terrestrial locale impacted by anthropogenic activities such as hydraulic fracturing, hydrocarbon extraction, wastewater disposal, or geothermal harvesting. Acoustic emission, seismic waves, or other detectable indicators of microscopic processes are sensed. A classifier is trained using time series of microscopic data along with corresponding data of critical state or failure events. In disclosed examples, random forests and artificial neural networks are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to live data from a fluid injection locale in order to assess a frictional state, assess seismic hazard, assess permeability, make predictions regarding a future fluid-driven failure event, or drive engineering solutions for mitigation or remediation. Variations are disclosed.

Johnson, Paul Allan↗

NASA NTRS2012

Failure Mode Analysis of V-Shaped Pyrotechnically Actuated Valves

Current V-shaped stainless steel pyrovalve initiators have rectified many of the deficiencies of the heritage Y-shaped aluminum design. However, a credible failure mode still exists for dual simultaneous initiator (NSI) firings in which low temperatures were detected at the booster cap and less consistent ignition was observed than when a single initiator was fired. In order to asses this issue, a numerical framework has been developed for predicting the flow through pyrotechnically actuated valves. This framework includes a fully coupled solution of the gas-phase equation with a non-equilibrium dispersed phase for solid particles as well as the capability to model conjugate gradient heat transfer to the booster cap. Through a hierarchy of increasingly complex simulations, a hypothesis for the failure mode of the nearly simultaneous dual NSI firings has been proven. The simulations indicate that the failure mode for simultaneous dual NSI firings may be caused by flow interactions between the flame channels. The shock waves from each initiator interact in the booster cavity resulting in a high pressure that prevents the gas and particulate velocity from rising in the booster cap region. This impedes the bulk of the particulate phase from impacting the booster cap and reduces the heat transfer to the booster cap since the particles do not impact it. Heat transfer calculations to the solid metal indicate that gas-phase convective heat transfer may not be adequate by itself and that energy transfer from the particulate phase may be crucial for the booster cap burn through.

Sachdev, Jai S.↗

DOE OSTI2021

Resiliency in numerical algorithm design for extreme scale simulations

Here this work is based on the seminar titled ‘Resiliency in Numerical Algorithm Design for Extreme Scale Simulations’ held March 1–6, 2020, at Schloss Dagstuhl, that was attended by all the authors. Advanced supercomputing is characterized by very high computation speeds at the cost of involving an enormous amount of resources and costs. A typical large-scale computation running for 48 h on a system consuming 20 MW, as predicted for exascale systems, would consume a million kWh, corresponding to about 100k Euro in energy cost for executing 10 23 floating-point operations. It is clearly unacceptable to lose the whole computation if any of the several million parallel processes fails during the execution. Moreover, if a single operation suffers from a bit-flip error, should the whole computation be declared invalid? What about the notion of reproducibility itself: should this core paradigm of science be revised and refined for results that are obtained by large-scale simulation? Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing checkpoint data all the way to background storage at frequent intervals will create intolerable overheads in runtime and energy consumption. Forecasts show that the mean time between failures could be lower than the time to recover from such a checkpoint, so that large calculations at scale might not make any progress if robust alternatives are not investigated. More advanced resilience techniques must be devised. The key may lie in exploiting both advanced system features as well as specific application knowledge. Research will face two essential questions: (1) what are the reliability requirements for a particular computation and (2) how do we best design the algorithms and software to meet these requirements? While the analysis of use cases can help understand the particular reliability requirements, the construction of remedies is currently wide open. One avenue would be to refine and improve on system- or application-level checkpointing and rollback strategies in the case an error is detected. Developers might use fault notification interfaces and flexible runtime systems to respond to node failures in an application-dependent fashion. Novel numerical algorithms or more stochastic computational approaches may be required to meet accuracy requirements in the face of undetectable soft errors. These ideas constituted an essential topic of the seminar. The goal of this Dagstuhl Seminar was to bring together a diverse group of scientists with expertise in exascale computing to discuss novel ways to make applications resilient against detected and undetected faults. In particular, participants explored the role that algorithms and applications play in the holistic approach needed to tackle this challenge. This article gathers a broad range of perspectives on the role of algorithms, applications and systems in achieving resilience for extreme scale simulations. The ultimate goal is to spark novel ideas and encourage the development of concrete solutions for achieving such resilience holistically.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Effects of chemical segregation on ductility-anisotropy in high strength Fe-Mn-Al-C lightweight austenitic steels

Ductile Fe-Mn-Al-C lightweight steels offer great potential as high specific strength materials for weight critical applications. In the present work, very high yield (~1 GPa) and ultimate tensile strength levels (~1.5 GPa) were obtained along the rolling and transverse directions of Fe-30Mn-8.5Al-0.9Si-0.9C-0.5Mo (wt.%), with high tensile elongation to failure of ~35%. This excellent mechanical behavior was attributed to the nanoscale, ordered κ-carbide precipitates formed during aging after rolling. However, tensile ductility was not isotropic through all directions of the rolled plate. In particular, the ductility was consistently lower than 5% true strain along the normal direction. Upon detailed microstructural investigations, micro-scale lamellar chemical micro-segregation bands were detected, which caused the observed anisotropic embrittlement along certain plate directions. These bands resulted in low failure strains along the normal direction since it is oriented perpendicular to these chemical segregation bands, resulting in a failure type reminiscent of delamination-induced cleavage fracture. Chemical segregation causes an inhomogeneous κ-carbide precipitate distribution upon aging, producing more pronounced mechanical anisotropy after peak-aging as compared to the solution heat treated samples. Surprisingly, deformation twinning was observed after tensile deformation of solution-heat treated specimens via electron-backscatter diffraction experiments, which is anomalous for a high stacking faulty energy (SFE) material. Further, it was proposed that chemical micro-segregation promotes locally different SFE regions, leading to deformation twinning in this nominally high SFE material. Overall, this work demonstrates the effects of chemical segregation on mechanical properties and microstructural evolution in a high performance, lightweight Fe-Mn-Al-C steel.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Advanced Monitoring and Control in the ANL METL Facility Using an Engineering Digital Twin

The potential benefits of using an engineering digital twin to achieve greater autonomy for monitoring and control functions in advanced reactors was investigated for the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The METL sodium purification system served as a representative system as it requires significant human in-the-loop interaction to accomplish its design function. The objective was to demonstrate how real-time operation could be automated while preserving oversight of the operator for ensuring that the system design functions are met. A digital twin model of the purification system was developed for both the cold trap purification loop and plugging meter diagnostic loop using information from the METL piping and instrumentation diagram (P&ID). Automated monitoring and diagnosis of component degradation in the METL facility was demonstrated in tests using the PRO-AID health monitoring software with the digital twin model incorporated in the library of components. Component failures were introduced and were successfully diagnosed in real time. These tests serve to demonstrate an advanced monitoring capability able to differentiate sensor degradation from component degradation, to generate a rank ordering of probabilities of different failure mechanisms that serves to circumvent the false alarm problem with current anomaly detection methods, and how facility monitoring can be transformed from anomaly detection to identification of a specific fault. Automated control of the purification system was demonstrated through simulations that exercised a model predictive controller designed using the digital twin model. Results of these simulations compared favorably with experimental data showing very good reference tracking response with negligible overshoot. In conclusion, these pilot tests and simulations successfully demonstrated the use of a digital twin for improved automation of monitoring and control. It was shown how the digital twin enables switching between control modes from cold trap operation where impurities are removed to plugging meter operation where impurity concentrations are measured.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Cold Spray Cobalt Magnetostrictive Electromagnetic Acoustic Transducers for High Temperature Structure Monitoring

The Department of Energy’s Advanced Sensors and Instrumentation program seeks to develop and qualify advanced sensors for the nuclear industry. Reliable high temperature and high radiation sensors for detection and characterization of structural flaws in pipes, vessels, and structurally critical components is a weakness for both conventional light water reactors with coolant T-hot approaching 350oC, and for advanced reactors with T-hot temperatures in excess of 500 to 800oC. Magnetostrictive Electromagnetic Acoustic Transducers using a cold spray cobalt coating have been proposed as a sensor design that can withstand these kinds of temperatures and radiation levels to serve as online sensors to detect flaws before cracks, pits, or erosion/corrosion flaws progress to through-wall failures. This report tests cold spray cobalt as part of a magnetostrictive EMAT for high temperature service. Cobalt is known to have strong magnetostrictive properties however the effect of cold spray application is not well studied. This program was surprised to discover that cold sprayed cobalt exhibited little or no magnetostrictive behavior until it was thermally annealed. Following annealing to 650oC however, cold spray cobalt did exhibit a magnetostrictive response. Work to date prior to this milestone report publication showed that magnetostrictive EMAT was successfully tested to 400oC with an alnico permanent magnet. The program plans to extend testing with an electromagnet to higher temperatures. This follow-on work will be reported under subsequent publications or as a revision to this report.

Glass, Samuel W.↗

NASA NTRS1974

Instrumentation for airbreathing propulsion; Proceedings of the Symposium, U.S. Naval Postgraduate School, Monterey, Calif., September 19-21, 1972

New developments and refinements in measuring techniques used for both ground testing and in-flight control, diagnosis, and monitoring of airbreathing combustors are analyzed in a number of papers. Some of the techniques studied include the application of laser velocimeters for flow measurements; on-the-shaft data systems for rotating engine components; total pressure averaging in pulsating flows; fiber optic and laser digital pressure transducers; holography of nozzles, jets, and spraying systems; application of the Raman effect fo flowfield diagnostics; holography of JP-4 droplets and combusting boron particles; the use of a laser-powered optical proximity probe in advanced turbofan engine development; pyrometry for measurement of surface temperature distribution on a rotating turbine blade; an ultrasonic turbine inlet gas temperature sensor; automatic detection and suppression of inlet buzz; and electrostatic probes for sensing incipient engine failure. Individual items are announced in this issue.

Fuhs, A. E.↗

NASA NTRS1974

System safety as applied to Skylab

Procedural and organizational guidelines used in accordance with NASA safety policy for the Skylab missions are outlined. The basic areas examined in the safety program for Skylab were the crew interface, extra-vehicular activity (EVA), energy sources, spacecraft interface, and hardware complexity. Fire prevention was a primary goal, with firefighting as backup. Studies of the vectorcardiogram and sleep monitoring experiments exemplify special efforts to prevent fire and shock. The final fire control study included material review, fire detection capability, and fire extinguishing capability. Contractors had major responsibility for system safety. Failure mode and effects analysis (FMEA) and equipment criticality categories are outlined. Redundancy was provided on systems that were critical to crew survival (category I). The five key checkpoints in Skylab hardware development are explained. Skylab rescue capability was demonstrated by preparations to rescue the Skylab 3 crew after their spacecraft developed attitude control problems.

Kleinknecht, K. S.↗

NASA NTRS1986

Ultrasonic stress wave characterization of composite materials

The work reported covers three simultaneous projects. The first project was concerned with: (1) establishing the sensitivity of the acousto-ultrasonic method for evaluating subtle forms of damage development in cyclically loaded composite materials, (2) establishing the ability of the acousto-ultrasonic method for detecting initial material imperfections that lead to localized damage growth and final specimen failure, and (3) characteristics of the NBS/Proctor sensor/receiver for acousto-ultrasonic evaluation of laminated composite materials. The second project was concerned with examining the nature of the wave propagation that occurs during acoustic-ultrasonic evaluation of composite laminates and demonstrating the role of Lamb or plate wave modes and their utilization for characterizing composite laminates. The third project was concerned with the replacement of contact-type receiving piezotransducers with noncontacting laser-optical sensors for acousto-ultrasonic signal acquisition.

Duke, J. C., Jr.↗

NASA NTRS1987

The KATE shell: An implementation of model-based control, monitor and diagnosis

The conventional control and monitor software currently used by the Space Center for Space Shuttle processing has many limitations such as high maintenance costs, limited diagnostic capabilities and simulation support. These limitations have caused the development of a knowledge based (or model based) shell to generically control and monitor electro-mechanical systems. The knowledge base describes the system's structure and function and is used by a software shell to do real time constraints checking, low level control of components, diagnosis of detected faults, sensor validation, automatic generation of schematic diagrams and automatic recovery from failures. This approach is more versatile and more powerful than the conventional hard coded approach and offers many advantages over it, although, for systems which require high speed reaction times or aren't well understood, knowledge based control and monitor systems may not be appropriate.

Cornell, Matthew↗

NASA NTRS1995

Space Shuttle Main Engine Propellant Path Leak Detection Using Sequential Image Processing

Initial research in this study using theoretical radiation transport models established that the occurrence of a leak is accompanies by a sudden but sustained change in intensity in a given region of an image. In this phase, temporal processing of video images on a frame-by-frame basis was used to detect leaks within a given field of view. The leak detection algorithm developed in this study consists of a digital highpass filter cascaded with a moving average filter. The absolute value of the resulting discrete sequence is then taken and compared to a threshold value to produce the binary leak/no leak decision at each point in the image. Alternatively, averaging over the full frame of the output image produces a single time-varying mean value estimate that is indicative of the intensity and extent of a leak. Laboratory experiments were conducted in which artificially created leaks on a simulated SSME background were produced and recorded from a visible wavelength video camera. This data was processed frame-by-frame over the time interval of interest using an image processor implementation of the leak detection algorithm. In addition, a 20 second video sequence of an actual SSME failure was analyzed using this technique. The resulting output image sequences and plots of the full frame mean value versus time verify the effectiveness of the system.

Smith, L. Montgomery↗

NASA NTRS2010

Demonstrating the Safety and Reliability of a New System or Spacecraft: Incorporating Analyses and Reviews of the Design and Processing in Determining the Number of Tests to be Conducted

Design Safety/Reliability is associated with the probability of no failure-causing faults existing in a design. Confidence in the non-existence of failure-causing faults is increased by performing tests with no failure. Reliability-Growth testing requirements are based on initial assurance and fault detection probability. Using binomial tables generally gives too many required tests compared to reliability-growth requirements. Reliability-Growth testing requirements are based on reliability principles and factors and should be used.

Vesely, William E.↗

DOE OSTI2023

Anomalous behavior detection by an artificial intelligence-enabled system with multiple correlated sensors

Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.

Serebryakov, Sergey↗