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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.

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At least 235 records · Page 13

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NASA NTRS1985

Determination of the gaseous hydrogen ductile-brittle transition in copper-nickel alloys

A series of copper-nickel alloys were fabricated, notched tensile specimens machined for each alloy, and the specimens tested in 34.5 MPa hydrogen and in air. A notched tensile ratio was determined for each alloy and the hydrogen environment embrittlement (HEE) determined for the alloys of 47.7 weight percent nickel to 73.5 weight percent nickel. Stacking fault probability and stacking fault energies were determined for each alloy using the x ray diffraction line shift and line profiles technique. Hydrogen environment embrittlement was determined to be influenced by stacking fault energies; however, the correlation is believed to be indirect and only partially responsible for the HEE behavior of these alloys.

Parr, R. A.↗

NASA NTRS1992

Failure behavior identification for a space antenna via neural networks

By using neural networks, a method for the failure behavior identification of a space antenna model is investigated. The proposed method uses three stages. If a fault is suspected by the first stage of fault detection, a diagnostic test is performed on the antenna. The diagnostic test results are used by the second and third stages to identify which fault occurred and to diagnose the extent of the fault, respectively. The first stage uses a multilayer perceptron, the second stage uses a multilayer perceptron and neural networks trained with the quadratic optimization algorithm, a novel training procedure, and the third stage uses backpropagation trained neural networks.

Sartori, Michael A.↗

NASA NTRS2015

A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.

structural model decomposition↗

NASA NTRS2016

Health Monitoring of a Planetary Rover Using Hybrid Particle Petri Nets

This paper focuses on the application of a Petri Net-based diagnosis method on a planetary rover prototype.The diagnosis is performed by using a model-based method in the context of health management of hybrid systems.In system health management, the diagnosis task aims at determining the current health state of a system and the fault occurrences that lead to this state. The Hybrid Particle Petri Nets (HPPN) formalism is used to model hybrid systems behavior and degradation, and to define the generation of diagnosers to monitor the health states of such systems under uncertainty. At any time, the HPPN-based diagnoser provides the current diagnosis represented by a distribution of beliefs over the health states. The health monitoring methodology is demonstrated on the K11 rover. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.

health monitoring↗

NASA NTRS1986

An artificial intelligence approach to onboard fault monitoring and diagnosis for aircraft applications

Real-time onboard fault monitoring and diagnosis for aircraft applications, whether performed by the human pilot or by automation, presents many difficult problems. Quick response to failures may be critical, the pilot often must compensate for the failure while diagnosing it, his information about the state of the aircraft is often incomplete, and the behavior of the aircraft changes as the effect of the failure propagates through the system. A research effort was initiated to identify guidelines for automation of onboard fault monitoring and diagnosis and associated crew interfaces. The effort began by determining the flight crew's information requirements for fault monitoring and diagnosis and the various reasoning strategies they use. Based on this information, a conceptual architecture was developed for the fault monitoring and diagnosis process. This architecture represents an approach and a framework which, once incorporated with the necessary detail and knowledge, can be a fully operational fault monitoring and diagnosis system, as well as providing the basis for comparison of this approach to other fault monitoring and diagnosis concepts. The architecture encompasses all aspects of the aircraft's operation, including navigation, guidance and controls, and subsystem status. The portion of the architecture that encompasses subsystem monitoring and diagnosis was implemented for an aircraft turbofan engine to explore and demonstrate the AI concepts involved. This paper describes the architecture and the implementation for the engine subsystem.

Schutte, P. C.↗

NASA NTRS1988

Chronic stress as a factor in aircraft mishaps

Naval aviation is an unusually stressful career because of the inherent demands of the work. Stress is recognized as a cause of mishaps which involve pilot error. A questionnaire was adapted from Rahe and Homes' list of stressful life events in order to determine the relationship between pilot behavioral, personality, and life change factors on the one hand and responsibility for accidents on the other. A number of factors regarding interpersonal relationships, changes in personal behavior, personality factors, and life changes were found to discriminate between pilots who were and were not at fault in accidents.

Alkov, Robert A.↗

NASA NTRS1980

Investigation of reliability attributes and accelerated stress factors on terrestrial solar cells

Three tasks were undertaken to investigate reliability attributes of terrestrial solar cells: (1) a study of the electrical behavior of cells in the second (reverse) quadrant; (2) the accelerated stress testing of three new state-of-the-art cells; and (3) the continued bias-temperature testing of four block 2 type silicon cells at 78 C and 135 C. Electrical characteristics measured in the second quadrant were determined to be a function of the cell's thermal behavior with breakdown depending on the initiation of localized heating. This implied that high breakdown cells may be more fault tolerant when forced to operate in the second quadrant, a result contrary to conventional thinking. The accelerated stress tests used in the first (power) quadrant were bias-temperature, bias-temperature-humidity, temperature-humidity, thermal shock, and thermal cycle. The new type cells measured included an EFG cell, a polycrystalline cell, and a Czochralski cell. Significant differences in the response to the various tests were observed between cell types. A microprocessed controlled, short interval solar cell tester was designed and construction initiated on a prototype.

Lathrop, J. W.↗

NASA NTRS1993

Modifying real convolutional codes for protecting digital filtering systems

A novel method is proposed for protecting digital filters from temporary and permanent failures that are not easily detected by conventional fault-tolerant computer design principles, on the basis of the error-detecting properties of real convolutional codes. Erroneous behavior is detected by externally comparing the calculated and regenerated parity samples. Great simplifications are obtainable by modifying the code structure to yield simplified parity channels with finite impulse response structures. A matrix equation involving the original parity values of the code and the polynomial of the digital filter's transfer function is formed, and row manipulations separate this equation into a set of homogeneous equations constraining the modifying scaling coefficients and another set which defines the code parity values' implementation.

Redinbo, G. R.↗

NASA NTRS1993

Reactive system verification case study: Fault-tolerant transputer communication

A reactive program is one which engages in an ongoing interaction with its environment. A system which is controlled by an embedded reactive program is called a reactive system. Examples of reactive systems are aircraft flight management systems, bank automatic teller machine (ATM) networks, airline reservation systems, and computer operating systems. Reactive systems are often naturally modeled (for logical design purposes) as a composition of autonomous processes which progress concurrently and which communicate to share information and/or to coordinate activities. Formal (i.e., mathematical) frameworks for system verification are tools used to increase the users' confidence that a system design satisfies its specification. A framework for reactive system verification includes formal languages for system modeling and for behavior specification and decision procedures and/or proof-systems for verifying that the system model satisfies the system specifications. Using the Ostroff framework for reactive system verification, an approach to achieving fault-tolerant communication between transputers was shown to be effective. The key components of the design, the decoupler processes, may be viewed as discrete-event-controllers introduced to constrain system behavior such that system specifications are satisfied. The Ostroff framework was also effective. The expressiveness of the modeling language permitted construction of a faithful model of the transputer network. The relevant specifications were readily expressed in the specification language. The set of decision procedures provided was adequate to verify the specifications of interest. The need for improved support for system behavior visualization is emphasized.

Crane, D. Francis↗

NASA NTRS1978

Local precision nets for monitoring movements of faults and large engineering structures

Along Bocono Fault were installed local high precision geodetic nets to observe the possible horizontal crustal deformations and movements. In the fault area there are few big structures which are also included in the mentioned investigation. In the near future, measurements shall be extended to other sites of Bocono Fault and also to the El Pilar Fault. In the same way and by similar methods high precision geodetic nets are applied in Venezuela to observe the behavior of big structures, as bridges and large dams and of earth surface deformations due to industrial activities.

Henneberg, H. G.↗

NASA NTRS1988

Performance evaluation of fault-tolerant systems with application to the IUS

A new method for quantitatively evaluating the performance of fault-tolerant systems is developed and applied to an example. The method assumes that the random failure and diagnostic decision behavior of the system can be modeled by a finite state Markov process. A performance value must be assigned to each of the states of the model. The method then generates the moments of the probability mass function of the cumulative performance and uses these to generate a maximum entropy approximation to the performance PMF. Some computational considerations are discussed. The method is applied to a typical mission for the Inertial Upper Stage to examine the attitude accuracy performance of the inertial system.

Missana, J.-O.↗

NASA NTRS1992

An expert system for monitoring dynamic aspects of RCS

An expert system (ARC) based on the MIMIC(2) framework is being developed for assistance in monitoring the Space Shuttle reaction control system (RCS) and in training RCS mission controllers. ARC has four components: a hypothesis generator, a model-constructor, a model-simulator and a matcher. The hypothesis generator examines current readings of sensors to generate a set of hypotheses. The model constructor generates models from these hypotheses. The simulator predicts future behaviors of the system. The matcher validates hypotheses by testing the predicted states against real data. The hypothesis generator postulates faults using model-based reasoning (MBR), learned rules from real data, and expert-defined rules. The simulator uses a qualitative QSIM model of the RCS which can handle both nominal modes as well as a variety of fault models. This allows the simulator to simulate the system even when one or more components have degraded. Finally, the matcher is responsible for comparing readings against predictions and calculating confidence in a particular hypothesis.

Kulkarni, Deepak↗

NASA NTRSDate not supplied

A Case Study on the Challenges and Opportunities for the Deployment of PHM Capabilities in Existing Engineering Systems

The field of Prognostics and Health Management (PHM) of engineering systems has experienced considerable growth over the last decade. From benefits associated with faster and more powerful hardware in the form of wireless sensors, edge devices, and general computing capabilities (GPU’s and cloud computing), to development of powerful algorithms for anomaly detection and remaining useful life (RUL) estimation, the number of engineering systems featuring advanced diagnostics and prognostics capabilities continues to grow at an increasingly faster pace. However, the deployment of PHM capabilities as part of the upgrade of existing engineering systems presents multiple challenges to the PHM practitioner charged with retrofitting such systems. Issues include a lack of specific instrumentation needed to capture the signals of interest; insufficient data and sampling rates required for fault detection and diagnosis, and for detection of failure/degradation indicators; and difficulties in the identification of a system’s nominal behavior as a result of age induced degradation. Today’s PHM practitioner must be able to quickly identify and assess these types of issues to effectively evaluate and select the optimal PHM strategies required to achieve the desired results. This paper presents results from the preliminary evaluation of the High-Pressure Gas Facility (HPGF) infrastructure at NASA’s Stennis Space Center in Hancock County, Mississippi. This evaluation is part of a feasibility study conducted prior to the deployment of prognostics and diagnostics capabilities in the pumps skids of the liquid nitrogen (LN2) system of the HPGF.

Condition Based Maintenance↗

NASA NTRSDate not supplied

Stochastic Verification by Analysis for Autonomous Systems Management Architecture (ASMA)

The Gateway Vehicle Systems Manager (VSM) is the top-level of a distributed, hierarchical software control system. VSM is data-driven and will make decisions related to mission, fault, resource management and vehicle control. These attributes combined with a high degree of autonomy make it susceptible to emergent behavior. In order to achieve the high level of confidence needed in this critical system, the VSM team has developed a multifaceted verification strategy employing traditional verification techniques, simulation, model checking, and runtime verification. Individual algorithms are verified using conventional testing and model checking using assume-guarantee contracts. A discrete event-based simulation approach is being developed to verify timelines. This presentation describes an enhancement to the verification approach using analysis to enhance system robustness by detecting and resolving the potential for emergent behavior. The verification by analysis employs a Software in the Loop (SITL) environment with real flight software executing on emulated processors, simulations of vehicle subsystems, flight dynamics, and human inputs. Since the possible input space and configuration data set are too large for exhaustive testing, a Monte Carlo approach is used to cover feasible scenarios, augmented with corner cases and known higher-risk scenarios. A key problem in using Monte Carlo-based system verification is evaluating test results to ensure that system behavior is correct. The presentation describes the approach the VSM team uses to monitor behavior for compliance with predetermined boundaries and to identify anomalous behavior for further analysis. This presentation describes the multi-level systems approach to verification, and the simulation-based layer that covers the feasible state space: 1. Overview of the Gateway VSM 2. Special challenges due to heterogeneous, hierarchical architecture 3. Modeling and simulation environment using flight software and system simulations 4. Developing input sets to ensure state-space coverage 5. Developing model and data configuration sets to ensure model coverage 6. Interpreting results without predetermined outcomes 7. Lessons learned and future work

Verification and Validation↗

NASA NTRS2010

Automatically Finding the Control Variables for Complex System Behavior

Testing large-scale systems is expensive in terms of both time and money. Running simulations early in the process is a proven method of finding the design faults likely to lead to critical system failures, but determining the exact cause of those errors is still time-consuming and requires access to a limited number of domain experts. It is desirable to find an automated method that explores the large number of combinations and is able to isolate likely fault points. Treatment learning is a subset of minimal contrast-set learning that, rather than classifying data into distinct categories, focuses on finding the unique factors that lead to a particular classification. That is, they find the smallest change to the data that causes the largest change in the class distribution. These treatments, when imposed, are able to identify the factors most likely to cause a mission-critical failure. The goal of this research is to comparatively assess treatment learning against state-of-the-art numerical optimization techniques. To achieve this, this paper benchmarks the TAR3 and TAR4.1 treatment learners against optimization techniques across three complex systems, including two projects from the Robust Software Engineering (RSE) group within the National Aeronautics and Space Administration (NASA) Ames Research Center. The results clearly show that treatment learning is both faster and more accurate than traditional optimization methods.

Gay, Gregory↗

NASA NTRS1991

Knowledge-based Autonomous Test Engineer (KATE)

Mathematical models of system components have long been used to allow simulators to predict system behavior to various stimuli. Recent efforts to monitor, diagnose, and control real-time systems using component models have experienced similar success. NASA Kennedy is continuing the development of a tool for implementing real-time knowledge-based diagnostic and control systems called KATE (Knowledge based Autonomous Test Engineer). KATE is a model-based reasoning shell designed to provide autonomous control, monitoring, fault detection, and diagnostics for complex engineering systems by applying its reasoning techniques to an exchangeable quantitative model describing the structure and function of the various system components and their systemic behavior.

Parrish, Carrie L.↗

NASA NTRSDate not supplied

Role of PHM in Autonomous Decision-Making: Aerospace applications

There is an increased need for onboard decision-making capabilities in cyber-physical systems be it in energy, automotive, aviation, space, or other industries as they aim for increased efficiency, resiliency, and mission assurance capabilities. Emerging next-gen technologies such as multi-rover planetary missions, distributed satellites, unmanned ground and aerial vehicle operations and smart grid systems rely on in-time risk assessment and autonomous decision-making. One critical piece of the autonomy puzzle is reliable prediction of system behavior under time-varying and potentially uncertain environmental conditions. Further, if agent states change during operation such as initiation of faults or degradation, reliable diagnostic tools need to be investigated. In this tutorial, we will revise approaches that integrates existing physics-based and data-driven models of agents interacting with probability models of the environment and component operation state. Role of existing PHM methodologies as they feed into decision-making under uncertainty will be studied. Balancing critical trade-offs between high-fidelity prognostic models, prediction time-horizons and the computational requirements for in-time cost-effective decision-making will be discussed through the implementation of surrogate models. Finally, the audience will be introduced to a real-time application of in-time trajectory planning of an unmanned aerial system (UAS) based on its PHM assessments under uncertain and varying wind conditions.

decision-making↗

NASA NTRS1983

Graphics enhanced computer emulation for improved timing-race and fault tolerance control system analysis

A computer simulation system has been developed for the Space Shuttle's advanced Centaur liquid fuel booster rocket, in order to conduct systems safety verification and flight operations training. This simulation utility is designed to analyze functional system behavior by integrating control avionics with mechanical and fluid elements, and is able to emulate any system operation, from simple relay logic to complex VLSI components, with wire-by-wire detail. A novel graphics data entry system offers a pseudo-wire wrap data base that can be easily updated. Visual subsystem operations can be selected and displayed in color on a six-monitor graphics processor. System timing and fault verification analyses are conducted by injecting component fault modes and min/max timing delays, and then observing system operation through a red line monitor.

Szatkowski, G. P.↗