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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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The REE project has developed a fault injection suite of tools and a methodology for experimentally determining system behavior statistics in the presence of application level SEU induced transient faults.
The Mars Exploration Rovers surface fault protection design was influenced by the fact that the solar-powered rovers must recharge their batteries during the day to survive the night. the rovers needed to autonomously maintain thermal stability, initiate safe and reliable communication with orbiting assets or directly to Earth, while maintaining energy balance. This paper will describe the system fault protection design for the surface phase of the mission.
This objectives of this slide presentation are to: (1) Share JPL experiences by describing the evolution of fault protection during its history in deep space exploration, (2) Examine issues of fault protection scope and implementation that affect missions today, and (3) Discuss solutions for the problems of today and tomorrow.
This is a companion presentation for a paper by the same name for the same conference. The objective of this paper is to shed some light on the fundamentals of fault tolerant design for GN&C. The common heritage of ideas behind both faulted and normal operation is explored, as is the increasingly indistinct line between these realms in complex missions. Techniques in common practice are then evaluated in this light to suggest a better direction for future efforts.
Disciplined approach to Fault Management (FM) has not always been emphasized by projects, contributing to major schedule and cost overruns: (1) Often faults aren't addressed until nominal spacecraft design is fairly stable. (2) Design relegated to after-the-fact patchwork, Band-Aid approach. Progress is being made on a number of fronts outside of Handbook effort: (1) Processes, Practices and Tools being developed at some Centers and Institutions (2) Management recognition. Constellation FM roles, Discovery/New Frontiers mission reviews (3) Potential Technology solutions. New approaches could avoid many current pitfalls (3a) New FM architectures, including model-based approach integrated with NASA's MBSE (Model-Based System Engineering) efforts (3b) NASA's Office of the Chief Technologist: FM identified in seven of NASA's 14 Space Technology Roadmaps. Opportunity to coalesce and establish thrust area to progressively develop new FM techniques. FM Handbook will help ensure that future missions do not encounter same FM-related problems as previous missions. Version 1 of the FM Handbook is a good start: (1) Still need Version 2 Agency-wide FM Handbook to expand Handbook to other areas, especially crewed missions. (2) Still need to reach out to other organizations to develop common understanding and vocabulary. Handbook doesn't/can't address all Workshop recommendations. Still need to identify how to address programmatic and infrastructure issues.
Functional fault models (FFMs) are qualitative representations of a system's failure space that are used to provide a diagnostic of the modeled system. An FFM simulates the failure effect propagation paths within a system between failure modes and observation points. These models contain a significant amount of information about the system including the design, operation and off nominal behavior. The development and verification of the models can be costly in both time and resources. In addition, models depicting similar components can be distinct, both in appearance and function, when created individually, because there are numerous ways of representing the failure space within each component. Generic application of FFMs has the advantages of software code reuse: reduction of time and resources in both development and verification, and a standard set of component models from which future system models can be generated with common appearance and diagnostic performance. This paper outlines the motivation to develop a generic modeling process for FFMs at the component level and the effort to implement that process through modeling conventions and a software tool. The implementation of this generic modeling process within a fault isolation demonstration for NASA's Advanced Ground System Maintenance (AGSM) Integrated Health Management (IHM) project is presented and the impact discussed.
Development of dependable systems relies on the ability of the system to determine and respond to off-nominal system behavior. Specification and development of these fault management capabilities must be done in a structured and principled manner to improve our understanding of these systems, and to make significant gains in dependability (safety, reliability and availability). Prior work has described a fundamental taxonomy and theory of System Health Management (SHM), and of its operational subset, Fault Management (FM). This conceptual foundation provides a basis to develop framework to design and implement FM design strategies that protect mission objectives and account for system design limitations. Selection of an SHM strategy has implications for the functions required to perform the strategy, and it places constraints on the set of possible design solutions. The framework developed in this paper provides a rigorous and principled approach to classifying SHM strategies, as well as methods for determination and implementation of SHM strategies. An illustrative example is used to describe the application of the framework and the resulting benefits to system and FM design and dependability.
This dissertation formulates the problem of fault detection and diagnosis of DC electric power systems for the application of autonomous spacecraft. The ability to accurately identify and isolate failures in the electrical power system is critical to ensure the reliability of a spacecraft. This problem becomes more pronounced during deep space missions that lack the ability to monitor from ground control. The current state of electrical power system fault supervision is insufficient to guarantee highly reliable and robust operation. To solve this issue, a combination of model-based and rules-based techniques are used in a hierarchical framework to improve the diagnostic performance of the spacecraft electrical power system. Noise, disturbances, and modeling errors are considered in the design of the method. Practical considerations related to the hardware and software are discussed for the flight application. A wide array of failure types are simulated in a series of experiments to assess the functionality of the design. The experiments showed that the methods used improved the diagnostic capability of the autonomous system while taking into account the limitations attributed to flight software requirements. The significance of this study is to provide a framework capable of advanced diagnostics of an electrical power system with little to no interaction from a human operator.
Over the past several years, techniques have been developed to discriminate between fault-prone software modules and those that are not, and to estimate a software system's residual fault content.
The Cassini spacecraft fault protection design includes fault monitors that use a state-space architecture to monitor the performance of spacecraft functions.
In developing a software system, we would like to estimate the way in which the fault content changes during its development, as well determine the locations having the highest concentration of faults.
Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs)at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases - distribution system state estimation (DSSE) and fault identification - are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.
Grid-forming (GFM) inverter controls have illustrated many desirable features to enable bulk integration of renewable sources into the future power grid. However, the performance of GFM inverters during unbalanced faults remains under-explored. In this paper, a novel current-limiting method is proposed for GFM inverters to handle unbalanced fault conditions while providing voltage support to the main grid. The proposed current limiter combines concepts of dynamic virtual-impedance and current-reference saturation limiting, all built-up in the stationary reference frame to achieve improved current-limiting performance under unbalanced load/fault conditions. System-wide full-order transient simulations with multiple GFM inverters demonstrate the potential of the method and benchmark the performance against state-of-the-art current limiters. Simulation results portray improved voltage balancing performance of the proposed method compared to both current-reference saturation and virtual-impedance limiting.
Thin gallium phosphide layers were deposited on (0 0 1) Silicon surfaces via organometallic vapor phase epitaxy and characterized by electron channeling contrast imaging (ECCI). Stacking fault pyramids at a density of up to 6 x 10 7 cm -2 were identified in the GaP nucleation layer by varying the diffraction conditions. We show that these defects originate at the GaP/Si interface and propagate on all four {1 1 1} planes. We observed that such stacking fault pyramids interact with the gliding of misfit dislocations during lattice-mismatched growth and enhance the threading dislocation density. The initial pulsing of TEGa and TBP during the nucleation of GaP on Silicon has been found to strongly influence the formation of those pyramidal defects. Changing the number of pulse cycles allowed us to lower their density by two orders of magnitude from 6 x 10 7 cm -2 to 4 x 10 5 cm -2 .
We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the $\mathcal{normal}$ waveforms and to improve the sensitivity of the model to identify a specific type of fault when we have limited samples for a given module type. We studied several Artificial Neural Network (ANN) architectures for our CVAE model and evaluated the model performance by looking at their loss landscape for stability and generalization. Our results for the Spallation Neutron Source (SNS) experimental data show that the trained model generalizes well to detecting multiple fault types for several HVCM module types. The results of this study can be used to improve the HVCM reliability and overall SNS uptime.
Ni 40 Co 20 Fe 10 Cr 10 Al 18 W 2 additively manufactured using laser powder bed fusion (LPBF) is among the toughest as-built alloys reported and is a promising candidate for use in extreme environments. However, its behavior under multi-megabar pressure regimes remains unexplored. We used femtosecond in situ X-ray diffraction to investigate the shock response of LPBF Ni40Co20Fe10Cr10Al18W2 under laser-driven shock compression and release. Our results reveal that the initial dual-phase face-centered + body-centered structure transforms to a single face-centered phase over a wide pressure range of 84±10 to 277±55 GPa, followed by a transition to a single body-centered phase at 431±48 GPa. We establish the Hugoniot equation-of-state of LPBF Ni 40 Co 20 Fe 10 Cr 10 Al 18 W 2 and compare it to the benchmark alloy AlCoCrFeNi 2.1 , demonstrating the effects of W-doping and increased Al content. High stacking fault probabilities, close to those measured in Au and Ag, are observed upon compression. A portion of the stacking faults are annihilated upon release to ambient pressure.
LiNiO 2 and cobalt-free ultrahigh-Ni content cathodes suffer from rapid capacity loss and severe chemomechanical degradation, especially when operated at high voltages. Here, by cycling LiNiO 2 up to 4.7 V, we report the atomic-scale observation of O1 faulted phase-induced deactivation of LiNiO 2 . We found that although a thin layer of O3 phase forms on the particle surface by reversible O3 → O1 transformation during discharge, the bulk interior still maintains the O1 faulted phase, leading to rapid capacity loss of LiNiO 2 . Moreover, the atomic configuration of the O1/O3 interface is investigated comprehensively. We found that the misfit along the c axes of the O1 and O3 phases results in the formation of misfit dislocations, whereby cation mixing is promoted at dislocation cores. Further, a transition zone with continuous shear along the a-b plane is uncovered between the O1 and O3 phases for the first time. Besides, severe oxygen loss-induced pore formation and concurrent rock salt transformation are also identified.
Machine learning models using seismic emissions as input can predict instantaneous fault characteristics such as displacement and friction in laboratory experiments, and slow slip in Earth. Here, we address whether the seismic/acoustic emission (AE) from laboratory experiments contains information about future frictional behavior. The approach uses a convolutional encoder-decoder containing a transformer model in the latent space, similar to models used for natural language processing. We test the model limits using progressively larger AE input time windows and progressively larger output friction time windows. The results demonstrate that very near-term friction predictions are indeed contained in the AE signal, and predictions are progressively worse farther into the future. The future predictions by the model of impending failure in the near-term are remarkably robust. This first effort predicting future fault frictional behavior with machine learning will aid in guiding efforts for applications in Earth.