Search NASA⌕ Search

SEARCH · Search NASA

Results for “multiplicative faults”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience

In this work, a post-disaster distribution system repair and restoration (DSRR) strategy is proposed to improve distribution system resilience. The DSRR strategy is formulated as a two-stage optimization. The first stage is a comprehensive co-optimization of repair crew scheduling, dynamic network reconfiguration, and distributed energy resource (DER) dispatch based on the forecast load profile. The goal is to minimize the accumulative operating cost caused by the load reduction payment as well as DER operating cost. In particular, since the number of available repair crews is usually smaller than the number of faulted lines after a disaster event, the DSRR strategy determines the optimal scheduling for repairing faulted lines. The second stage is a re-dispatch of the DER power output and load shedding based on the real-time load demand of each bus. The proposed algorithm is validated by case studies of the IEEE 33-bus and 123-bus test systems. We consider those scenarios in which faults occur in multiple heavy-loaded feeders. The simulation results demonstrate that the DSRR strategy effectively coordinate the repair scheduling, network reconfiguration and load shedding to minimize the operating cost.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Explaining System-Level Prognostics with Established Machine Learning Methods

System-level prognostics is crucial for ensuring reliability and enabling predictive maintenance in complex systems with interconnected components. This study presents a framework that integrates data-driven methods to predict the remaining useful life (RUL) of a subsystem under multiple and concurrent faults within a nuclear power plant system with explainable artificial intelligence (XAI). A nuclear power plant (NPP) operation was simulated to model the degradation behavior of NPP components, and four machine learning models—Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory (LSTM)—were evaluated for prognostics with a novel system RUL parameter. The LSTM model demonstrated potential superior repeatability, while SHAP (SHapley Additive exPlanations) for explainability provided consistent and trustworthy global explanations. In contrast, LIME (Local Interpretable Model-agnostic Explanations) offered localized interpretability but showed reduced stability for sequential data. Key findings include the interplay between component-level degradation and system-wide performance, with LSTM effectively capturing these dynamics through sequence-level predictions. The XAI techniques enhanced transparency by identifying critical features influencing model predictions and aligning with domain knowledge. Furthermore, this framework has significant implications for improving trust and understanding in predictive maintenance, particularly in safety-critical industries like nuclear energy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Accelerating cavity fault prediction using deep learning at Jefferson Laboratory

Abstract Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model’s ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

43 PARTICLE ACCELERATORS↗
NASA NTRS1990

Monitoring and control of spacecraft systems using procedural reasoning

Research concerned with automating the monitoring and control of spacecraft systems is discussed. In particular, the application of SRI's Procedural Reasoning System (PRS) to the handling of malfunctions in the Reaction Control System (RCS) of NASA's Space Shuttle is examined. Unlike traditional monitoring and control systems, PRS is able to reason about and perform complex tasks in a very flexible and robust manner, somewhat in the manner of a human assistant. Using various RCS malfunctions as examples (including sensor faults, leaking components, multiple alarms, and regulator and jet failures), it is shown how PRS manages to combine both goal-directed reasoning and the ability to react rapidly to unanticipated changes in its environment. In conclusion, some important issues in the design of PRS are reviewed and future enhancements are indicated.

Georgeff, Michael P.↗
NASA NTRS1990

Real-time reasoning - The monitoring and control of spacecraft systems

Research concerned with automating the monitoring and control of spacecraft systems is discussed. In particular, the application the Procedural Reasoning System (PRS) to the handling of malfunctions in the Reaction Control System (RCS) of NASAs space shuttle is studied. Unlike traditional monitoring and control systems, PRS is able to reason about and perform complex tasks in a very flexible and robust manner, somewhat in the manner of a human assistant. Using various RCS malfunctions as examples (including sensor faults, leaking components, multiple alarms, and regulator and jet failures), it is shown how PRS manages to combine both goal-directed reasoning and the ability to react rapidly to unanticipated changes in its environment. Some important issues in the design of PRS are reviewed, and future enhancements are indicated.

Georgeff, Michael P.↗
NASA NTRS1991

The use of programmable logic controllers (PLC) for rocket engine component testing

Application of PLCs to the rocket engine component testing at a new Stennis Space Center Component Test Facility is suggested as an alternative to dedicated specialized computers. The PLC systems are characterized by rugged design, intuitive software, fault tolerance, flexibility, multiple end device options, networking capability, and built-in diagnostics. A distributed PLC-based system is projected to be used for testing LH2/LOx turbopumps required for the ALS/NLS rocket engines.

Nail, William↗
NASA NTRS2005

Application of a Constant Gain Extended Kalman Filter for In-Flight Estimation of Aircraft Engine Performance Parameters

An approach based on the Constant Gain Extended Kalman Filter (CGEKF) technique is investigated for the in-flight estimation of non-measurable performance parameters of aircraft engines. Performance parameters, such as thrust and stall margins, provide crucial information for operating an aircraft engine in a safe and efficient manner, but they cannot be directly measured during flight. A technique to accurately estimate these parameters is, therefore, essential for further enhancement of engine operation. In this paper, a CGEKF is developed by combining an on-board engine model and a single Kalman gain matrix. In order to make the on-board engine model adaptive to the real engine s performance variations due to degradation or anomalies, the CGEKF is designed with the ability to adjust its performance through the adjustment of artificial parameters called tuning parameters. With this design approach, the CGEKF can maintain accurate estimation performance when it is applied to aircraft engines at offnominal conditions. The performance of the CGEKF is evaluated in a simulation environment using numerous component degradation and fault scenarios at multiple operating conditions.

Kobayashi, Takahisa↗
NASA NTRS2004

NASA Tech Briefs, February 2004

Topics include: Simulation Testing of Embedded Flight Software; Improved Indentation Test for Measuring Nonlinear Elasticity; Ultraviolet-Absorption Spectroscopic Biofilm Monitor; Electronic Tongue for Quantitation of Contaminants in Water; Radar for Measuring Soil Moisture Under Vegetation; Modular Wireless Data-Acquisition and Control System; Microwave System for Detecting Ice on Aircraft; Routing Algorithm Exploits Spatial Relations; Two-Finger EKG Method of Detecting Evasive Responses; Updated System-Availability and Resource-Allocation Program; Routines for Computing Pressure Drops in Venturis; Software for Fault-Tolerant Matrix Multiplication; Reproducible Growth of High-Quality Cubic-SiC Layers; Nonlinear Thermoelastic Model for SMAs and SMA Hybrid Composites; Liquid-Crystal Thermosets, a New Generation of High-Performance Liquid-Crystal Polymers; Formulations for Stronger Solid Oxide Fuel-Cell Electrolytes; Simulation of Hazards and Poses for a Rocker-Bogie Rover; Autonomous Formation Flight; Expandable Purge Chambers Would Protect Cryogenic Fittings; Wavy-Planform Helicopter Blades Make Less Noise; Miniature Robotic Spacecraft for Inspecting Other Spacecraft; Miniature Ring-Shaped Peristaltic Pump; Compact Plasma Accelerator; Improved Electrohydraulic Linear Actuators; A Software Architecture for Semiautonomous Robot Control; Fabrication of Channels for Nanobiotechnological Devices; Improved Thin, Flexible Heat Pipes; Miniature Radioisotope Thermoelectric Power Cubes; Permanent Sequestration of Emitted Gases in the Form of Clathrate Hydrates; Electrochemical, H2O2-Boosted Catalytic Oxidation System; Electrokinetic In Situ Treatment of Metal-Contaminated Soil; Pumping Liquid Oxygen by Use of Pulsed Magnetic Fields; Magnetocaloric Pumping of Liquid Oxygen; Tailoring Ion-Thruster Grid Apertures for Greater Efficiency; and Lidar for Guidance of a Spacecraft or Exploratory Robot.

Source record↗
NASA NTRS1992

Branch recovery with compiler-assisted multiple instruction retry

In processing systems where rapid recovery from transient faults is important, schemes for multiple instruction rollback recovery may be appropriate. Multiple instruction retry has been implemented in hardware by researchers and also in mainframe computers. This paper extends compiler-assisted instruction retry to a broad class of code execution failures. Five benchmarks were used to measure the performance penalty of hazard resolution. Results indicate that the enhanced pure software approach can produce performance penalties consistent with existing hardware techniques. A combined compiler/hardware resolution strategy is also described and evaluated. Experimental results indicate a lower performance penalty than with either a totally hardware or totally software approach.

Alewine, N. J.↗
DOE OSTI2022

A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems

We report faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2003

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

Kobayashi, Takahisa↗
DOE OSTI2023

Simultaneous estimation of multiple eigenvalues with short-depth quantum circuit on early fault-tolerant quantum computers

We introduce a multi-modal, multi-level quantum complex exponential least squares (MM-QCELS) method to simultaneously estimate multiple eigenvalues of a quantum Hamiltonian on early fault-tolerant quantum computers. Our theoretical analysis demonstrates that the algorithm exhibits Heisenberg-limited scaling in terms of circuit depth and total cost. Notably, the proposed quantum circuit utilizes just one ancilla qubit, and with appropriate initial state conditions, it achieves significantly shorter circuit depths compared to circuits based on quantum phase estimation (QPE). Numerical results suggest that compared to QPE, the circuit depth can be reduced by around two orders of magnitude under several settings for estimating ground-state and excited-state energies of certain quantum systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Blockchain for Fault-Tolerant Grid Operations

Radial topology and vast geographic coverage make distribution systems prone to widespread power outages upon the failure of a single (or multiple) upstream component. Fault-handling algorithms depend heavily on correct estimations of the system’s state to effectively isolate the affected area and reduce the number of affected customers while maintaining operational safety. The work described here leverages the core features of distributed, consensus-based decision-making processes and the immutability of blockchain, and demonstrates their value in improving fault-tolerant grid operations. In this work, blockchain was used to create a trusted data-sharing platform that enables independent actors to reconstruct the system state; this enables distributed resources to make intelligent decisions with limited knowledge. Although the process requires data sharing, its algorithms have been designed to limit the amount of private information that is exchanged, which helps preserve business-sensitive data and maintain customer privacy. In addition, by reducing the information that must be shared, the communication requirements are also reduced; (however, an in-depth analysis of the communication requirements is beyond the scope of this project). The proposed use cases are intended to represent a foundational basis for third parties to develop functional solutions that can eventually be deployed in the field. To further provide guidance, the envisioned use cases have incorporated design requirements that consider the blockchain characteristics and a need to limit information from surrounding resources, which preserve the assumption and the possibility that such resources could belong to different entities. This report presents a detailed design of the three use cases with the tools needed to enable the analysis being tested. The implemented gross error detection method can detect mismatches when the error exceeds 3.8 times the sensor’s rated accuracy. Detection of the circuit breaker state successfully identified the correct states across all simulation tests. A distribution-system power-flow solution in the simulator OpenDSS generally possesses a convergency tolerance of 0.01% on the voltage magnitude. The evaluation of possible reconnection using voltage magnitude—preserving the data ownership—has a voltage magnitude difference smaller than 0.001% from the OpenDSS result. The results preserving data ownership have a difference within the expected power flow tolerance with full knowledge of the system, which surpasses expectations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1989

Test aspects of the JPL Viterbi decoder

The generation of test vectors and design-for-test aspects of the Jet Propulsion Laboratory (JPL) Very Large Scale Integration (VLSI) Viterbi decoder chip is discussed. Each processor integrated circuit (IC) contains over 20,000 gates. To achieve a high degree of testability, a scan architecture is employed. The logic has been partitioned so that very few test vectors are required to test the entire chip. In addition, since several blocks of logic are replicated numerous times on this chip, test vectors need only be generated for each block, rather than for the entire circuit. These unique blocks of logic have been identified and test sets generated for them. The approach employed for testing was to use pseudo-exhaustive test vectors whenever feasible. That is, each cone of logid is tested exhaustively. Using this approach, no detailed logic design or fault model is required. All faults which modify the function of a block of combinational logic are detected, such as all irredundant single and multiple stuck-at faults.

Breuer, M. A.↗
NASA NTRS1989

Mechanics of distributed fault and block rotation

Paleomagnetic data, structural geology, and rock mechanics are used to explore the validity and significance of the block rotation concept. The analysis is based on data from Northern Israel, where fault slip and spacing are used to predict block rotation; the Mojave Desert, with well documented strike-slip sets; the Lake Mead, Nevada fault system with well-defined sets of strike-slip faults; and the San Gabriel Mountains domain with a multiple set of strike-slip faults. The results of the analysis indicate that block rotations can have a profound influence on the interpretation of geodetic measurments and the inversion of geodetic data. Furthermore, the block rotations and domain boundaries may be involved in creating the heterogeneities along active fault systems which may be responsible for the initiation and termination of earthquake rupture.

Nur, A.↗
NASA NTRS2019

A Multiple Model Based Approach for Deep Space Power System Fault Diagnosis

Improving protection and health management capabilities onboard the electrical power system (EPS) for spacecraft is essential for ensuring safe and reliable conditions for deep space human exploration. Electrical protection and control technologies on the National Aeronautics and Space Administration's (NASA's) current human space platform relies heavily on ground support to monitor and diagnose power systems and failures. As communication bandwidth diminishes for deep space applications, a transformation in system monitoring and control becomes necessary to maintain high reliability of electric power service. This paper presents a novel approach for on-line power system security monitoring for autonomous deep space spacecraft.

Autonomous Power Controller↗