Results for “fault prediction” — Page 12

NASA NTRS2005

Discrete element simulations of gravitational volcanic deformation: Deformation structures and geometries - 1

We have carried out two-dimensional particle dynamics simulations of granular piles subject to frictional Coulomb failure criteria to gain a first-order understanding of different modes of gravitational deformation within volcanoes. Under uniform basal and internal strength conditions, granular piles grow self-similarly, developing distinctive stratigraphies, morphologies, and structures. Piles constructed upon cohesive substrates exhibit particle avalanching, forming outward dipping strata and angle of repose slopes. Systematic decreases in basal strength lead to progressively deeper and steeper internal detachment faults and slip along a basal decollement; landslide forms grade from shallow slumps to deep-seated landslide and, finally, to axial subsidence and outward flank displacements, or volcanic spreading. Surface slopes decrease and develop concave up morphologies with decreasing decollement strength; depositional layers tilt progressively inward. Spatial variations in basal strength cause lateral transitions in pile structure, stratigraphy, and morphology. This approximation of volcanoes as Coulomb granular piles reproduces the richness of deformational structures and surface morphologies in many volcanic settings. The gentle slopes of Hawaiian volcanoes and Olympus Mons on Mars suggest weak basal decollements that enable volcanic spreading. High-angle normal faults, favored above weak decollements, are interpreted in both settings and may explain catastrophic sector collapse in Hawaii and broad aureole deposits surrounding Olympus Mons. In contrast, steeper slopes and shallow detachment faults predominate in the Canary Islands, thought to lack a weak decollement, favoring smaller, more frequent slope failures than predicted for Hawaii. The numerical results provide a useful predictive tool for interpreting dynamic behavior and associated geologic hazards of active volcanoes.

Morgan, Julia K.↗

NASA NTRS2015

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Gas Turbine Engines↗

NASA NTRS2015

Circadian Effects on Simple Components of Complex Task Performance

The goal of this study was to advance understanding and prediction of the impact of circadian rhythm on aspects of complex task performance during unexpected automation failures, and subsequent fault management. Participants trained on two tasks: a process control simulation, featuring automated support; and a multi-tasking platform. Participants then completed one task in a very early morning (circadian night) session, and the other during a late afternoon (circadian day) session. Small effects of time of day were seen on simple components of task performance, but impacts on more demanding components, such as those that occur following an automation failure, were muted relative to previous studies where circadian rhythm was compounded with sleep deprivation and fatigue. Circadian low participants engaged in compensatory strategies, rather than passively monitoring the automation. The findings and implications are discussed in the context of a model that includes the effects of sleep and fatigue factors.

circadian effects↗

NASA NTRS2015

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Gas Turbine Engines↗

NASA NTRS2016

Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics

This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.

Diagnosis↗

NASA NTRS1988

Semi-Markov adjunction to the Computer-Aided Markov Evaluator (CAME)

The rule-based Computer-Aided Markov Evaluator (CAME) program was expanded in its ability to incorporate the effect of fault-handling processes into the construction of a reliability model. The fault-handling processes are modeled as semi-Markov events and CAME constructs and appropriate semi-Markov model. To solve the model, the program outputs it in a form which can be directly solved with the Semi-Markov Unreliability Range Evaluator (SURE) program. As a means of evaluating the alterations made to the CAME program, the program is used to model the reliability of portions of the Integrated Airframe/Propulsion Control System Architecture (IAPSA 2) reference configuration. The reliability predictions are compared with a previous analysis. The results bear out the feasibility of utilizing CAME to generate appropriate semi-Markov models to model fault-handling processes.

Rosch, Gene↗

NASA NTRS1975

LANDSAT imagery analysis: An aid for predicting landslide prone areas for highway construction

The most obvious landform features of geologic significance revealed on LANDSAT imagery are linear trends or lineaments. These trends were found to correspond, at least to a large degree, with unmapped faults or complex fracture zones. LANDSAT imagery analysis in northern Arkansas revealed a lineament complex which provides a remarkable correlation with landslide-prone areas along major highway routes. The weathering properties of various rock types, which are considered in designing stable cut slopes and drainage structures, appear to be adversely influenced by the location and trends of LANDSAT defined lineaments. Geologic interpretation of LANDSAT imagery, where applicable and utilized effectively, provides the highway engineer with a tool for predicting and evaluating landslide-prone areas.

Macdonald, H. C.↗

NASA NTRS2013

Mission Data System Java Edition Version 7

The Mission Data System framework defines closed-loop control system abstractions from State Analysis including interfaces for state variables, goals, estimators, and controllers that can be adapted to implement a goal-oriented control system. The framework further provides an execution environment that includes a goal scheduler, execution engine, and fault monitor that support the expression of goal network activity plans. Using these frameworks, adapters can build a goal-oriented control system where activity coordination is verified before execution begins (plan time), and continually during execution. Plan failures including violations of safety constraints expressed in the plan can be handled through automatic re-planning. This version optimizes a number of key interfaces and features to minimize dependencies, performance overhead, and improve reliability. Fault diagnosis and real-time projection capabilities are incorporated. This version enhances earlier versions primarily through optimizations and quality improvements that raise the technology readiness level. Goals explicitly constrain system states over explicit time intervals to eliminate ambiguity about intent, as compared to command-oriented control that only implies persistent intent until another command is sent. A goal network scheduling and verification process ensures that all goals in the plan are achievable before starting execution. Goal failures at runtime can be detected (including predicted failures) and handled by adapted response logic. Responses can include plan repairs (try an alternate tactic to achieve the same goal), goal shedding, ignoring the fault, cancelling the plan, or safing the system.

Reinholtz, William K.↗

NASA NTRS2004

Advanced Diagnostic System on Earth Observing One

In this infusion experiment, the Livingstone 2 (L2) model-based diagnosis engine, developed by the Computational Sciences division at NASA Ames Research Center, has been uploaded to the Earth Observing One (EO-1) satellite. L2 is integrated with the Autonomous Sciencecraft Experiment (ASE) which provides an on-board planning capability and a software bridge to the spacecraft's 1773 data bus. Using a model of the spacecraft subsystems, L2 predicts nominal state transitions initiated by control commands, monitors the spacecraft sensors, and, in the case of failure, isolates the fault based on the discrepant observations. Fault detection and isolation is done by determining a set of component modes, including most likely failures, which satisfy the current observations. All mode transitions and diagnoses are telemetered to the ground for analysis. The initial L2 model is scoped to EO-1's imaging instruments and solid state recorder. Diagnostic scenarios for EO-1's nominal imaging timeline are demonstrated by injecting simulated faults on-board the spacecraft. The solid state recorder stores the science images and also hosts: the experiment software. The main objective of the experiment is to mature the L2 technology to Technology Readiness Level (TRL) 7. Experiment results are presented, as well as a discussion of the challenging technical issues encountered. Future extensions may explore coordination with the planner, and model-based ground operations.

Hayden, Sandra C.↗

NASA NTRS2005

Health Management Applications for International Space Station

Traditional mission and vehicle management involves teams of highly trained specialists monitoring vehicle status and crew activities, responding rapidly to any anomalies encountered during operations. These teams work from the Mission Control Center and have access to engineering support teams with specialized expertise in International Space Station (ISS) subsystems. Integrated System Health Management (ISHM) applications can significantly augment these capabilities by providing enhanced monitoring, prognostic and diagnostic tools for critical decision support and mission management. The Intelligent Systems Division of NASA Ames Research Center is developing many prototype applications using model-based reasoning, data mining and simulation, working with Mission Control through the ISHM Testbed and Prototypes Project. This paper will briefly describe information technology that supports current mission management practice, and will extend this to a vision for future mission control workflow incorporating new ISHM applications. It will describe ISHM applications currently under development at NASA and will define technical approaches for implementing our vision of future human exploration mission management incorporating artificial intelligence and distributed web service architectures using specific examples. Several prototypes are under development, each highlighting a different computational approach. The ISStrider application allows in-depth analysis of Caution and Warning (C&W) events by correlating real-time telemetry with the logical fault trees used to define off-nominal events. The application uses live telemetry data and the Livingstone diagnostic inference engine to display the specific parameters and fault trees that generated the C&W event, allowing a flight controller to identify the root cause of the event from thousands of possibilities by simply navigating animated fault tree models on their workstation. SimStation models the functional power flow for the ISS Electrical Power System and can predict power balance for nominal and off-nominal conditions. SimStation uses realtime telemetry data to keep detailed computational physics models synchronized with actual ISS power system state. In the event of failure, the application can then rapidly diagnose root cause, predict future resource levels and even correlate technical documents relevant to the specific failure. These advanced computational models will allow better insight and more precise control of ISS subsystems, increasing safety margins by speeding up anomaly resolution and reducing,engineering team effort and cost. This technology will make operating ISS more efficient and is directly applicable to next-generation exploration missions and Crew Exploration Vehicles.

Alena, Richard↗

NASA NTRS2007

Reducing a Knowledge-Base Search Space When Data Are Missing

This software addresses the problem of how to efficiently execute a knowledge base in the presence of missing data. Computationally, this is an exponentially expensive operation that without heuristics generates a search space of 1 + 2n possible scenarios, where n is the number of rules in the knowledge base. Even for a knowledge base of the most modest size, say 16 rules, it would produce 65,537 possible scenarios. The purpose of this software is to reduce the complexity of this operation to a more manageable size. The problem that this system solves is to develop an automated approach that can reason in the presence of missing data. This is a meta-reasoning capability that repeatedly calls a diagnostic engine/model to provide prognoses and prognosis tracking. In the big picture, the scenario generator takes as its input the current state of a system, including probabilistic information from Data Forecasting. Using model-based reasoning techniques, it returns an ordered list of fault scenarios that could be generated from the current state, i.e., the plausible future failure modes of the system as it presently stands. The scenario generator models a Potential Fault Scenario (PFS) as a black box, the input of which is a set of states tagged with priorities and the output of which is one or more potential fault scenarios tagged by a confidence factor. The results from the system are used by a model-based diagnostician to predict the future health of the monitored system.

James, Mark↗

NASA NTRS1988

Advanced Earth-to-orbit propulsion technology program overview: Impact of civil space technology initiative

The NASA Earth-to-Orbit (ETO) Propulsion Technology Program is dedicated to advancing rocket engine technologies for the development of fully reusable engine systems that will enable space transportation systems to achieve low cost, routine access to space. The program addresses technology advancements in the areas of engine life extension/prediction, performance enhancements, reduced ground operations costs, and in-flight fault tolerant engine operations. The primary objective is to acquire increased knowledge and understanding of rocket engine chemical and physical processes in order to evolve more realistic analytical simulations of engine internal environments, to derive more accurate predictions of steady and unsteady loads, and using improved structural analyses, to more accurately predict component life and performance, and finally to identify and verify more durable advanced design concepts. In addition, efforts were focused on engine diagnostic needs and advances that would allow integrated health monitoring systems to be developed for enhanced maintainability, automated servicing, inspection, and checkout, and ultimately, in-flight fault tolerant engine operations.

Stephenson, Frank W., Jr.↗

NASA NTRS2013

Vehicle Integrated Prognostic Reasoner (VIPR) Metric Report

This document outlines a set of metrics for evaluating the diagnostic and prognostic schemes developed for the Vehicle Integrated Prognostic Reasoner (VIPR), a system-level reasoner that encompasses the multiple levels of large, complex systems such as those for aircraft and spacecraft. VIPR health managers are organized hierarchically and operate together to derive diagnostic and prognostic inferences from symptoms and conditions reported by a set of diagnostic and prognostic monitors. For layered reasoners such as VIPR, the overall performance cannot be evaluated by metrics solely directed toward timely detection and accuracy of estimation of the faults in individual components. Among other factors, overall vehicle reasoner performance is governed by the effectiveness of the communication schemes between monitors and reasoners in the architecture, and the ability to propagate and fuse relevant information to make accurate, consistent, and timely predictions at different levels of the reasoner hierarchy. We outline an extended set of diagnostic and prognostics metrics that can be broadly categorized as evaluation measures for diagnostic coverage, prognostic coverage, accuracy of inferences, latency in making inferences, computational cost, and sensitivity to different fault and degradation conditions. We report metrics from Monte Carlo experiments using two variations of an aircraft reference model that supported both flat and hierarchical reasoning.

Cornhill, Dennis↗

NASA NTRS2011

Deep Moonquake Focal Mechanisms: Recovery and Implications

A defining characteristic of deep moonquakes is their tendency to occur with tidal periodicity, prompting previous studies to infer that they are related to the buildup and release of tidal stress within the Moon. In studies of tidal forcing, a key constraint is the focal mechanism: the fault parameters describing the type of failure moonquakes represent. The quality of the lunar seismic data and the limited source/receiver geometries of the Apollo seismic network prohibit the determination of deep moonquake fault parameters using first-motion polarities, as is typically done in terrestrial seismology. Without being able to resolve tidal stress onto a known failure plane, we can examine only gross qualities of the tidal stress tensor with respect to moonquake occurrence, so we cannot fully address the role of tidal stress in moonquake generation. We will examine the extent to which shear (S) and compression (P) wave amplitude ratios can constrain moonquake fault geometry by determining whether, for a given cluster, there exists a focal mechanism that can produce a radiation pattern consistent with the amplitudes measured by the Apollo instruments. Amplitudes are read in the ray coordinate frame, directly from seismograms for which the P and S arrivals are clearly identifiable on all long-period channels of the four Apollo stations. We apply an empirical station correction to account for site effects and the differences between P- and S-wave attenuation. Instead of focusing on the best fitting solution only, we formulate the inverse problem using a falsification criterion: all source orientations that do not reproduce the observed SV/P ratios within an error margin derived from the uncertainty of amplitude readings are rejected. All others are accepted as possible solutions. The inversion is carried out using an exhaustive grid search on a regular grid with predefined step size, encompassing all possible combinations of strike, dip and slip. To assess the sensitivity of the inversion for the uncertainty of the lunar interior structure, we carry out repeated inversions with different velocity structures. Our data set consist of a total of 106 events from 25 deep moonquake clusters. The largest contribution of 37 events originates from the most active cluster, A001, while other clusters are represented by 1 to 9 events. Since the definition of a cluster implies that all events share the same source orientation, a comparison of the inversion results of all events from one cluster will reduce ambiguities of the inversion. Once we obtain a suite of fault parameters for a given source, we can attempt to further constrain the focal mechanism with refined analyses of tidal stresses and predictions based on synthetic seismograms.

Knapmeyer, Martin↗

NASA NTRS2012

Real-Time Projection to Verify Plan Success During Execution

The Mission Data System provides a framework for modeling complex systems in terms of system behaviors and goals that express intent. Complex activity plans can be represented as goal networks that express the coordination of goals on different state variables of the system. Real-time projection extends the ability of this system to verify plan achievability (all goals can be satisfied over the entire plan) into the execution domain so that the system is able to continuously re-verify a plan as it is executed, and as the states of the system change in response to goals and the environment. Previous versions were able to detect and respond to goal violations when they actually occur during execution. This new capability enables the prediction of future goal failures; specifically, goals that were previously found to be achievable but are no longer achievable due to unanticipated faults or environmental conditions. Early detection of such situations enables operators or an autonomous fault response capability to deal with the problem at a point that maximizes the available options. For example, this system has been applied to the problem of managing battery energy on a lunar rover as it is used to explore the Moon. Astronauts drive the rover to waypoints and conduct science observations according to a plan that is scheduled and verified to be achievable with the energy resources available. As the astronauts execute this plan, the system uses this new capability to continuously re-verify the plan as energy is consumed to ensure that the battery will never be depleted below safe levels across the entire plan.

Wagner, David A.↗

NASA NTRS2014

Current Pressure Transducer Application of Model-based Prognostics Using Steady State Conditions

Prognostics is the process of predicting a system's future states, health degradation/wear, and remaining useful life (RUL). This information plays an important role in preventing failure, reducing downtime, scheduling maintenance, and improving system utility. Prognostics relies heavily on wear estimation. In some components, the sensors used to estimate wear may not be fast enough to capture brief transient states that are indicative of wear. For this reason it is beneficial to be capable of detecting and estimating the extent of component wear using steady-state measurements. This paper details a method for estimating component wear using steady-state measurements, describes how this is used to predict future states, and presents a case study of a current/pressure (I/P) Transducer. I/P Transducer nominal and off-nominal behaviors are characterized using a physics-based model, and validated against expected and observed component behavior. This model is used to map observed steady-state responses to corresponding fault parameter values in the form of a lookup table. This method was chosen because of its fast, efficient nature, and its ability to be applied to both linear and non-linear systems. Using measurements of the steady state output, and the lookup table, wear is estimated. A regression is used to estimate the wear propagation parameter and characterize the damage progression function, which are used to predict future states and the remaining useful life of the system.

Fault Estimation↗

NASA NTRS1981

Strain buildup and release, earthquake prediction and selection of VBL sites for margins of the north Pacific

During the past year, the grant supported research on several aspects of crustal deformation. The relation between earthquake displacements and fault dimensions was studied in an effort to find scaling laws that relate static parameters such as slip and stress drop to the dimensions of the rupture. Several implications of the static relations for the dynamic properties of earthquakes such as rupture velocity and dynamic stress drop were proposed. A theoretical basis for earthquake related phenomena associated with slow rupture growth or propagation, such as delayed multiple events, was developed using the stress intensity factor defined in fracture mechanics and experimental evidence from studies of crack growth by stress corrosion. Finally, extensive studies by Japanese geologists have established the offset across numerous faults in Japan over the last one hundred thousand years. These observations of intraplate faulting are being used to establish the spatial variations of the average strain rate of subregions in southern Japan.

Scholz, C. H.↗

NASA NTRS2017

Data Collection and Analysis Software Development for Rotor Dynamics Testing in Spin Laboratory

Gas turbine engine components undergo high rotational loading another complex environmental conditions. Such operating environment leads these components to experience damages and cracks that can cause catastrophic failure during flights. There are traditional crack detections and health monitoring methodologies currently being used which rely on periodic routine maintenances, nondestructive inspections that often times involve engine and components dis-assemblies. These methods do not also offer adequate information about the faults, especially, if these faults at subsurface or not clearly evident. At NASA Glenn research center, the rotor dynamics laboratory is presently involved in developing newer techniques that are highly dependent on sensor technology to enable health monitoring and prediction of damage and cracks in rotor disks. These approaches are noninvasive and relatively economical. Spin tests are performed using a subscale test article mimicking turbine rotor disk undergoing rotational load. Non-contact instruments such as capacitive and microwave sensors are used to measure the blade tip gap displacement and blade vibrations characteristics in an attempt develop a physics based model to assess/predict the faults in the rotor disk. Data collection is a major component in this experimental-analytical procedure and as a result, an upgrade to an older version of the data acquisition software which is based on LabVIEW program has been implemented to support efficiently running tests and analyze the results. Outcomes obtained from the tests data and related experimental and analytical rotor dynamics modeling including key features of the updated software are presented and discussed.

Rotor Dynamics↗