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NASA NTRS2009

An Indirect Adaptive Control Scheme in the Presence of Actuator and Sensor Failures

The problem of controlling a system in the presence of unknown actuator and sensor faults is addressed. The system is assumed to have groups of actuators, and groups of sensors, with each group consisting of multiple redundant similar actuators or sensors. The types of actuator faults considered consist of unknown actuators stuck in unknown positions, as well as reduced actuator effectiveness. The sensor faults considered include unknown biases and outages. The approach employed for fault detection and estimation consists of a bank of Kalman filters based on multiple models, and subsequent control reconfiguration to mitigate the effect of biases caused by failed components as well as to obtain stability and satisfactory performance using the remaining actuators and sensors. Conditions for fault identifiability are presented, and the adaptive scheme is applied to an aircraft flight control example in the presence of actuator failures. Simulation results demonstrate that the method can rapidly and accurately detect faults and estimate the fault values, thus enabling safe operation and acceptable performance in spite of failures.

Sun, Joy Z.↗

NASA NTRS2012

Damage Characterization Method for Structural Health Management Using Reduced Number of Sensor Inputs

The development of validated multidisciplinary Integrated Vehicle Health Management (IVHM) tools, technologies, and techniques to enable detection, diagnosis, prognosis, and mitigation in the presence of adverse conditions during flight will provide effective solutions to deal with safety related challenges facing next generation aircraft. The adverse conditions include loss of control caused by environmental factors, actuator and sensor faults or failures, and damage conditions. A major concern in these structures is the growth of undetected damage (cracks) due to fatigue and low velocity foreign impacts that can reach a critical size during flight, resulting in loss of control of the aircraft. Hence, development of efficient methodologies to determine the presence, location, and severity of damage in critical structural components is highly important in developing efficient structural health management systems.

Krishnamurthy, T.↗

NASA NTRS2011

Error Mitigation of Point-to-Point Communication for Fault-Tolerant Computing

Fault tolerant systems require the ability to detect and recover from physical damage caused by the hardware s environment, faulty connectors, and system degradation over time. This ability applies to military, space, and industrial computing applications. The integrity of Point-to-Point (P2P) communication, between two microcontrollers for example, is an essential part of fault tolerant computing systems. In this paper, different methods of fault detection and recovery are presented and analyzed.

Akamine, Robert L.↗

NASA NTRS2011

Fault-Tolerant, Radiation-Hard DSP

Commercial digital signal processors (DSPs) for use in high-speed satellite computers are challenged by the damaging effects of space radiation, mainly single event upsets (SEUs) and single event functional interrupts (SEFIs). Innovations have been developed for mitigating the effects of SEUs and SEFIs, enabling the use of very-highspeed commercial DSPs with improved SEU tolerances. Time-triple modular redundancy (TTMR) is a method of applying traditional triple modular redundancy on a single processor, exploiting the VLIW (very long instruction word) class of parallel processors. TTMR improves SEU rates substantially. SEFIs are solved by a SEFI-hardened core circuit, external to the microprocessor. It monitors the health of the processor, and if a SEFI occurs, forces the processor to return to performance through a series of escalating events. TTMR and hardened-core solutions were developed for both DSPs and reconfigurable field-programmable gate arrays (FPGAs). This includes advancement of TTMR algorithms for DSPs and reconfigurable FPGAs, plus a rad-hard, hardened-core integrated circuit that services both the DSP and FPGA. Additionally, a combined DSP and FPGA board architecture was fully developed into a rad-hard engineering product. This technology enables use of commercial off-the-shelf (COTS) DSPs in computers for satellite and other space applications, allowing rapid deployment at a much lower cost. Traditional rad-hard space computers are very expensive and typically have long lead times. These computers are either based on traditional rad-hard processors, which have extremely low computational performance, or triple modular redundant (TMR) FPGA arrays, which suffer from power and complexity issues. Even more frustrating is that the TMR arrays of FPGAs require a fixed, external rad-hard voting element, thereby causing them to lose much of their reconfiguration capability and in some cases significant speed reduction. The benefits of COTS high-performance signal processing include significant increase in onboard science data processing, enabling orders of magnitude reduction in required communication bandwidth for science data return, orders of magnitude improvement in onboard mission planning and critical decision making, and the ability to rapidly respond to changing mission environments, thus enabling opportunistic science and orders of magnitude reduction in the cost of mission operations through reduction of required staff. Additional benefits of COTS-based, high-performance signal processing include the ability to leverage considerable commercial and academic investments in advanced computing tools, techniques, and infra structure, and the familiarity of the science and IT community with these computing environments.

Czajkowski, David↗

NASA NTRS1984

Hot-spot investigations of utility scale panel configurations

The causes of array faults and efforts to mitigate their effects are examined. Research is concentrated on the panel for the 900 kw second phase of the Sacramento Municipal Utility District (SMUD) project. The panel is designed for hot spot tolerance without comprising efficiency under normal operating conditions. Series/paralleling internal to each module improves tolerance in the power quadrant to cell short or open circuits. Analtyical methods are developed for predicting worst case shade patterns and calculating the resultant cell temperature. Experiments conducted on a prototype panel support the analytical calculations.

Arnett, J. C.↗

NASA NTRS2009

Probabilistic Risk Assessment for Decision Making During Spacecraft Operations

Decisions made during the operational phase of a space mission often have significant and immediate consequences. Without the explicit consideration of the risks involved and their representation in a solid model, it is very likely that these risks are not considered systematically in trade studies. Wrong decisions during the operational phase of a space mission can lead to immediate system failure whereas correct decisions can help recover the system even from faulty conditions. A problem of special interest is the determination of the system fault protection strategies upon the occurrence of faults within the system. Decisions regarding the fault protection strategy also heavily rely on a correct understanding of the state of the system and an integrated risk model that represents the various possible scenarios and their respective likelihoods. Probabilistic Risk Assessment (PRA) modeling is applicable to the full lifecycle of a space mission project, from concept development to preliminary design, detailed design, development and operations. The benefits and utilities of the model, however, depend on the phase of the mission for which it is used. This is because of the difference in the key strategic decisions that support each mission phase. The focus of this paper is on describing the particular methods used for PRA modeling during the operational phase of a spacecraft by gleaning insight from recently conducted case studies on two operational Mars orbiters. During operations, the key decisions relate to the commands sent to the spacecraft for any kind of diagnostics, anomaly resolution, trajectory changes, or planning. Often, faults and failures occur in the parts of the spacecraft but are contained or mitigated before they can cause serious damage. The failure behavior of the system during operations provides valuable data for updating and adjusting the related PRA models that are built primarily based on historical failure data. The PRA models, in turn, provide insight into the effect of various faults or failures on the risk and failure drivers of the system and the likelihood of possible end case scenarios, thereby facilitating the decision making process during operations. This paper describes the process of adjusting PRA models based on observed spacecraft data, on one hand, and utilizing the models for insight into the future system behavior on the other hand. While PRA models are typically used as a decision aid during the design phase of a space mission, we advocate adjusting them based on the observed behavior of the spacecraft and utilizing them for decision support during the operations phase.

dynamic fault trees↗

NASA NTRSDate not supplied

Synthetic Failure Mode Generation for Resilience Analysis and Failure Mechanism Discovery

Traditional risk-based design processes seek to mitigate operational hazards by manually identifying possible faults and corresponding mitigation strategies—a tedious process which critically relies on the designer’s limited knowledge. Resilience-based design, on the other hand, seeks to embody generic hazard-mitigating properties in the system to mitigate unknown hazards, often by modelling the system's response to potential hazardous events. This work adapts this approach to the traditional risk-based design process to synthetically generate hazardous modes, by representing them as a unique combination of internal component health-states which can then be injected and simulated in a model of the system failure dynamics. The design process may then reduce the risk of unknown internal hazards by iteratively mitigating the effects of these modes. The performance of this approach is evaluated in a model of an autonomous rover, where cluster analysis shows that elaborating the space of synthetic faults in the drive system using this approach uncovers a wider range of possible hazardous trajectories and failure consequences within each trajectory. However, this increase in hazard information comes at a high computational expense, highlighting the need for advanced, efficient methods to search and sample the hazard space.

Simulation↗

NASA NTRSDate not supplied

Synthetic Failure Mode Generation for Resilience Analysis and Failure Mechanism Discovery

Simulation↗

NASA NTRSDate not supplied

A New Test Methodology for Evaluating Fire Risk Due to Electrical Faults in Oxygen-Enriched Environments

This paper discusses a new test methodology for assessing the safe use of materials found in spacecraft that may be exposed to electrical faults, as performed at the NASA White Sands Test Facility. “Wire-break” testing, modeled after the Cleveland Open Cup method, identifies the conditions under which pyrolyzed material transitions from a brief flash to a propagating flame. The approach focuses on fuel mass liberated through pyrolysis, mixed with oxygen, and ignited by a piloted ignition (spark due to fusion of wire) to achieve flame propagation with the least energy. The test approach attempts to find energy density thresholds derived using I²t values from current-induced heating of various test materials and wire characteristics. The application of the data provides a useful approach to mitigating fire risks in space systems by correlating threshold data to real-world designs and scaling factors, including oxygen pressure, wire diameter, heat sinking, and contained smoke volume. Several case studies, including Apollo missions and hypothetical scenarios, illustrate practical applications of these thresholds to enhance fire safety design considerations in space environments.

methodology↗

NASA NTRS2011

Towards a State Based Control Architecture for Large Telescopes: Laying a Foundation at the VLT

Large telescopes are characterized by a high level of distribution of control-related tasks and will feature diverse data flow patterns and large ranges of sampling frequencies; there will often be no single, fixed server-client relationship between the control tasks. the architecture is also challenged by the task of integrating heterogeneous subsystems which will be delivered by multiple different contractors. Due to the high number of distributed components, the control system needs to effectively detect errors and faults, impede their propagation, and accurately mitigate them in the shortest time possible, enabling the service to be restored. The presented Data-Driven Architecture is based on a decentralized approach with an end-to-end integration of disparate, independently developed software components. These components employ a high-performance standards-based communication middle-ware infrastructure, based on the Data Distribution Service. A set of rules and principles, based on JPL's State Analysis method and architecture, are use to constrain component-to component interactions, where the Control System and System Under Control are clearly separated. State Analysis provide a model-based process for capturing system and software requirements and design, greatly reducing the gap between the requirements on software specified by systems engineers and the implementation by software engineers. The method and architecture has been field tested at the Very Large Telescope, where it has been integrated into an operational system.

European Extremely Large Telescope (E-ELT)↗

DOE OSTI2025

Unsupervised Clustering of Microseismic Events and Focal Mechanism Analysis at the CO 2 Injection Site in Decatur, Illinois

Characterization of induced microseismicity at a carbon dioxide (CO 2 ) storage site is critical for preserving reservoir integrity and mitigating seismic hazards. We apply a multilevel machine learning (ML) approach that combines the nonnegative matrix factorization and hidden Markov model to extract spectral representations of microseismic events and cluster them to identify seismic patterns at the Illinois Basin-Decatur Project. Unlike traditional waveform correlation methods, this approach leverages spectral characteristics of first arrivals to improve event classification and detect previously undetected planes of weakness. By integrating ML-based clustering with focal mechanism analysis, we resolve small-scale fault structures that are below the detection limits of conventional seismic imaging. Our findings reveal temporal bursts of microseismicity associated with brittle failure, providing insights into the spatio-temporal evolution of fault reactivation during CO 2 injection. This approach enhances seismic monitoring capabilities at CO 2 injection sites by improving fault characterization beyond the resolution of standard geophysical surveys.

Willis, Rachel Marie [Sandia National Laboratories↗

NASA NTRS1996

Neural Networks for Flight Control

Neural networks are being developed at NASA Ames Research Center to permit real-time adaptive control of time varying nonlinear systems, enhance the fault-tolerance of mission hardware, and permit online system reconfiguration. In general, the problem of controlling time varying nonlinear systems with unknown structures has not been solved. Adaptive neural control techniques show considerable promise and are being applied to technical challenges including automated docking of spacecraft, dynamic balancing of the space station centrifuge, online reconfiguration of damaged aircraft, and reducing cost of new air and spacecraft designs. Our experiences have shown that neural network algorithms solved certain problems that conventional control methods have been unable to effectively address. These include damage mitigation in nonlinear reconfiguration flight control, early performance estimation of new aircraft designs, compensation for damaged planetary mission hardware by using redundant manipulator capability, and space sensor platform stabilization. This presentation explored these developments in the context of neural network control theory. The discussion began with an overview of why neural control has proven attractive for NASA application domains. The more important issues in control system development were then discussed with references to significant technical advances in the literature. Examples of how these methods have been applied were given, followed by projections of emerging application needs and directions.

Jorgensen, Charles C.↗

DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗

NASA NTRS2001

Fundamentals of Digital Engineering: Designing for Reliability

The concept of designing for reliability will be introduced along with a brief overview of reliability, redundancy and traditional methods of fault tolerance is presented, as applied to current logic devices. The fundamentals of advanced circuit design and analysis techniques will be the primary focus. The introduction will cover the definitions of key device parameters and how analysis is used to prove circuit correctness. Basic design techniques such as synchronous vs asynchronous design, metastable state resolution time/arbiter design, and finite state machine structure/implementation will be reviewed. Advanced topics will be explored such as skew-tolerant circuit design, the use of triple-modular redundancy and circuit hazards, device transients and preventative circuit design, lock-up states in finite state machines generated by logic synthesizers, device transient characteristics, radiation mitigation techniques. worst-case analysis, the use of timing analyzer and simulators, and others. Case studies and lessons learned from spaceflight designs will be given as examples

Katz, R.↗

NASA NTRS2006

ISHM Decision Analysis Tool: Operations Concept

The state-of-the-practice Shuttle caution and warning system warns the crew of conditions that may create a hazard to orbiter operations and/or crew. Depending on the severity of the alarm, the crew is alerted with a combination of sirens, tones, annunciator lights, or fault messages. The combination of anomalies (and hence alarms) indicates the problem. Even with much training, determining what problem a particular combination represents is not trivial. In many situations, an automated diagnosis system can help the crew more easily determine an underlying root cause. Due to limitations of diagnosis systems,however, it is not always possible to explain a set of alarms with a single root cause. Rather, the system generates a set of hypotheses that the crew can select from. The ISHM Decision Analysis Tool (IDAT) assists with this task. It presents the crew relevant information that could help them resolve the ambiguity of multiple root causes and determine a method for mitigating the problem. IDAT follows graphical user interface design guidelines and incorporates a decision analysis system. I describe both of these aspects.

Source record↗

DOE OSTI2026

Quantum-classical embedding via ghost Gutzwiller approximation for enhanced simulations of correlated electron systems

Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources. Quantum embedding methods offer a promising route by reducing computational complexity through the mapping of bulk systems onto effective impurity models, allowing more feasible simulations on pre- and early-fault-tolerant quantum devices. Here, this work develops a quantum-classical embedding framework based on the ghost Gutzwiller approximation to enable quantum-enhanced simulations of ground-state properties and spectral functions of correlated electron systems. Circuit complexity is analyzed using an adaptive variational quantum algorithm on a statevector simulator, applied to the infinite-dimensional Hubbard model with increasing ghost mode numbers from 3 to 5, resulting in circuit depths growing from 16 to 104. Noise effects are examined using a realistic error model, revealing significant impact on the spectral weight of the Hubbard bands. To mitigate these effects, the Iceberg quantum error detection code is employed, achieving up to 40% error reduction in simulations. Finally, the accuracy of the density matrix estimation and the derived spectral function is benchmarked on IBM and Quantinuum quantum hardware, featuring distinct qubit-connectivity and employing multiple levels of error mitigation techniques.

Chen, I-Chi [Ames Laboratory (AMES), Ames, IA (Uni↗

NASA NTRS2015

Fault Management Architectures and the Challenges of Providing Software Assurance

The satellite systems Fault Management (FM) is focused on safety, the preservation of assets, and maintaining the desired functionality of the system. How FM is implemented varies among missions. Common to most is system complexity due to a need to establish a multi-dimensional structure across hardware, software and operations. This structure is necessary to identify and respond to system faults, mitigate technical risks and ensure operational continuity. These architecture, implementation and software assurance efforts increase with mission complexity. Because FM is a systems engineering discipline with a distributed implementation, providing efficient and effective verification and validation (VV) is challenging. A breakout session at the 2012 NASA Independent Verification Validation (IVV) Annual Workshop titled VV of Fault Management: Challenges and Successes exposed these issues in terms of VV for a representative set of architectures. NASA's IVV is funded by NASA's Software Assurance Research Program (SARP) in partnership with NASA's Jet Propulsion Laboratory (JPL) to extend the work performed at the Workshop session. NASA IVV will extract FM architectures across the IVV portfolio and evaluate the data set for robustness, assess visibility for validation and test, and define software assurance methods that could be applied to the various architectures and designs. This work focuses efforts on FM architectures from critical and complex projects within NASA. The identification of particular FM architectures, visibility, and associated VVIVV techniques provides a data set that can enable higher assurance that a satellite system will adequately detect and respond to adverse conditions. Ultimately, results from this activity will be incorporated into the NASA Fault Management Handbook providing dissemination across NASA, other agencies and the satellite community. This paper discusses the approach taken to perform the evaluations and preliminary findings from the research including identification of FM architectures, visibility observations, and methods utilized for VVIVV.

Fault Management↗

NASA NTRS2015

Fault Management Architectures and the Challenges of Providing Software Assurance

Fault Management (FM) is focused on safety, the preservation of assets, and maintaining the desired functionality of the system. How FM is implemented varies among missions. Common to most missions is system complexity due to a need to establish a multi-dimensional structure across hardware, software and spacecraft operations. FM is necessary to identify and respond to system faults, mitigate technical risks and ensure operational continuity. Generally, FM architecture, implementation, and software assurance efforts increase with mission complexity. Because FM is a systems engineering discipline with a distributed implementation, providing efficient and effective verification and validation (V&V) is challenging. A breakout session at the 2012 NASA Independent Verification & Validation (IV&V) Annual Workshop titled "V&V of Fault Management: Challenges and Successes" exposed this issue in terms of V&V for a representative set of architectures. NASA's Software Assurance Research Program (SARP) has provided funds to NASA IV&V to extend the work performed at the Workshop session in partnership with NASA's Jet Propulsion Laboratory (JPL). NASA IV&V will extract FM architectures across the IV&V portfolio and evaluate the data set, assess visibility for validation and test, and define software assurance methods that could be applied to the various architectures and designs. This SARP initiative focuses efforts on FM architectures from critical and complex projects within NASA. The identification of particular FM architectures and associated V&V/IV&V techniques provides a data set that can enable improved assurance that a system will adequately detect and respond to adverse conditions. Ultimately, results from this activity will be incorporated into the NASA Fault Management Handbook providing dissemination across NASA, other agencies and the space community. This paper discusses the approach taken to perform the evaluations and preliminary findings from the research.

Fault Management↗