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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Autonomous Inspection of Electrical Transmission Structures with Airborne UV Sensors - NASA Report on Dominion Virginia Power Flights of November 2016

The report details test and measurement flights to demonstrate autonomous UAV inspection of high voltage electrical transmission structures. A UAV built with commercial, off-the-shelf hardware and software, supplemented with custom sensor logging software, measured ultraviolet emissions from a test generator placed on a low-altitude substation and a medium-altitude switching tower. Since corona discharge precedes catastrophic electrical faults on high-voltage structures, detection and geolocation of ultraviolet emissions is needed to develop a UAV-based self-diagnosing power grid. Signal readings from an onboard ultraviolet sensor were validated during flight with a commercial corona camera. Geolocation was accomplished with onboard GPS; the UAV position was logged to a local ground station and transmitted in real time to a NASA server for tracking in the national airspace.

Moore, Andrew J.↗
NASA NTRS2018

Autonomous Inspection of Electrical Transmission Structures with Airborne UV Sensors and Automated Air Traffic Management

This report details test and measurement flights to demonstrate autonomous UAV (Unmanned Aerial Vehicle) inspection of high-voltage electrical transmission structures. A UAV built with commercial, off-the-shelf hardware and software, supplemented with custom sensors and logging software, measured ultraviolet (UV) emissions from a test generator placed on a low-altitude substation and a medium-altitude switching tower. Since corona discharge precedes catastrophic electrical faults on high-voltage structures, detection and geolocation of ultraviolet emissions is needed to develop a UAV-based self-diagnosing power grid. Signal readings from an onboard ultraviolet sensor were validated during flight with a commercial corona camera. Geolocation was accomplished with onboard GPS; the UAV position was logged to a local ground station and transmitted in real time to a NASA server for tracking in the national airspace. The method has practicality and relevance but not adequacy; either improved UAV position determination technology or increased sensor range is needed to enable broad deployment of this method.

Moore, Andrew J.↗
NASA NTRS2012

HyDE Framework for Stochastic and Hybrid Model-Based Diagnosis

Hybrid Diagnosis Engine (HyDE) is a general framework for stochastic and hybrid model-based diagnosis that offers flexibility to the diagnosis application designer. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. Several alternative algorithms are available for the various steps in diagnostic reasoning. This approach is extensible, with support for the addition of new modeling paradigms as well as diagnostic reasoning algorithms for existing or new modeling paradigms. HyDE is a general framework for stochastic hybrid model-based diagnosis of discrete faults; that is, spontaneous changes in operating modes of components. HyDE combines ideas from consistency-based and stochastic approaches to model- based diagnosis using discrete and continuous models to create a flexible and extensible architecture for stochastic and hybrid diagnosis. HyDE supports the use of multiple paradigms and is extensible to support new paradigms. HyDE generates candidate diagnoses and checks them for consistency with the observations. It uses hybrid models built by the users and sensor data from the system to deduce the state of the system over time, including changes in state indicative of faults. At each time step when observations are available, HyDE checks each existing candidate for continued consistency with the new observations. If the candidate is consistent, it continues to remain in the candidate set. If it is not consistent, then the information about the inconsistency is used to generate successor candidates while discarding the candidate that was inconsistent. The models used by HyDE are similar to simulation models. They describe the expected behavior of the system under nominal and fault conditions. The model can be constructed in modular and hierarchical fashion by building component/subsystem models (which may themselves contain component/ subsystem models) and linking them through shared variables/parameters. The component model is expressed as operating modes of the component and conditions for transitions between these various modes. Faults are modeled as transitions whose conditions for transitions are unknown (and have to be inferred through the reasoning process). Finally, the behavior of the components is expressed as a set of variables/ parameters and relations governing the interaction between the variables. The hybrid nature of the systems being modeled is captured by a combination of the above transitional model and behavioral model. Stochasticity is captured as probabilities associated with transitions (indicating the likelihood of that transition being taken), as well as noise on the sensed variables.

Narasimhan, Sriram↗
NASA NTRS1983

Unified analysis methods for a fault tolerant redundant strapdown inertial measurement unit

The use of a redundant strapdown inertial measurement unit (RSDIMU) has been considered as a source of sensor information for future aircraft. It is pointed out that failure detection and isolation (FDI) should be accomplished at the sensor level to ensure the accurate transfer of information to elements of the integrated avionics system. The present investigation is concerned with the impact of the FDI algorithm on system reliability and vice versa. A description is given of a technique to identify and assess critical areas of reliability requirements for the RSDIMU. Attention is given to the development of an FDI algorithm to meet these requirements.

Morrell, F. R.↗
NASA NTRS2010

Propulsion Health Monitoring of a Turbine Engine Disk Using Spin Test Data

This paper considers data collected from an experimental study using high frequency capacitive sensor technology to capture blade tip clearance and tip timing measurements in a rotating turbine engine-like-disk-to predict the disk faults and assess its structural integrity. The experimental results collected at a range of rotational speeds from tests conducted at the NASA Glenn Research Center s Rotordynamics Laboratory are evaluated using multiple data-driven anomaly detection techniques to identify abnormalities in the disk. Further, this study presents a select evaluation of an online health monitoring scheme of a rotating disk using high caliber sensors and test the capability of the in-house spin system.

Abdul-Aziz, Ali↗
NASA NTRS1975

Tug avionics system overview

The recently defined Tug avionics system takes maximum advantage of projected technology advances to attain: low system weight; power system capacity essentially independent of mission duration; sensors for rendezvous, docking, and navigation update; all attitude communications; onboard checkout and redundancy management; and modular fault tolerant computer control. The requirements, selection trades, and configurations are discussed for the major subsystems: data management, guidance navigation and control, communications, rendezvous and docking, and electrical power. The integrated avionics system and the interfaces with the payload, Shuttle and ground are described.

Raaberg, M. T.↗
NASA NTRS1985

The influence of sampling rate on the dynamics and control of aeronautical systems

It is shown in the design of aeronautical control systems that sampling rate variations give rise to migrations of the discrete eigenvalues for both the plant and the controllers. In general, these migrations tend to degrade simulation fidelity and the overall performance of Ultrareliable Fault Tolerant Control Systems (UFTCS). These systems consist of a redundant asynchronous network of microprocessors which use sensor information to generate the servo commands that drive actuators, thrust linkages, etc. A change in the sampling period of a single microprocessor will, in addition to causing an eigenvalue shift, alter the magnitude of the servo command issued by the controller. The influence of these changes and the effect of initial conditions and skew are considered.

Howard, J. C.↗
NASA NTRS1989

A design philosophy for multi-layer neural networks with applications to robot control

A system is proposed which receives input information from many sensors that may have diverse scaling, dimension, and data representations. The proposed system tolerates sensory information with faults. The proposed self-adaptive processing technique has great promise in integrating the techniques of artificial intelligence and neural networks in an attempt to build a more intelligent computing environment. The proposed architecture can provide a detailed decision tree based on the input information, information stored in a long-term memory, and the adapted rule-based knowledge. A mathematical model for analysis will be obtained to validate the cited hypotheses. An extensive software program will be developed to simulate a typical example of pattern recognition problem. It is shown that the proposed model displays attention, expectation, spatio-temporal, and predictory behavior which are specific to the human brain. The anticipated results of this research project are: (1) creation of a new dynamic neural network structure, and (2) applications to and comparison with conventional multi-layer neural network structures. The anticipated benefits from this research are vast. The model can be used in a neuro-computer architecture as a building block which can perform complicated, nonlinear, time-varying mapping from a multitude of input excitory classes to an output or decision environment. It can be used for coordinating different sensory inputs and past experience of a dynamic system and actuating signals. The commercial applications of this project can be the creation of a special-purpose neuro-computer hardware which can be used in spatio-temporal pattern recognitions in such areas as air defense systems, e.g., target tracking, and recognition. Potential robotics-related applications are trajectory planning, inverse dynamics computations, hierarchical control, task-oriented control, and collision avoidance.

Vadiee, Nader↗
NASA NTRS1992

Health management and controls for earth to orbit propulsion systems

Fault detection and isolation for advanced rocket engine controllers are discussed focusing on advanced sensing systems and software which significantly improve component failure detection for engine safety and health management. Aerojet's Space Transportation Main Engine controller for the National Launch System is the state of the art in fault tolerant engine avionics. Health management systems provide high levels of automated fault coverage and significantly improve vehicle delivered reliability and lower preflight operations costs. Key technologies, including the sensor data validation algorithms and flight capable spectrometers, have been demonstrated in ground applications and are found to be suitable for bridging programs into flight applications.

Bickford, R. L.↗
NASA NTRS2013

Towards Real-time, On-board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

Unmanned aerial systems (UASs) can only be deployed if they can effectively complete their missions and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. In this paper, we design a real-time, on-board system health management (SHM) capability to continuously monitor sensors, software, and hardware components for detection and diagnosis of failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and/or software signals; (2) signal analysis, preprocessing, and advanced on the- fly temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software due to instrumentation. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual data from the NASA Swift UAS, an experimental all-electric aircraft.

System & Software Health Management↗
NASA NTRS2019

Networked Array Recorder (NeAR) Microphones for Field-Deployed Phased Arrays

An innovative edge-computing concept known as NeAR (Networked Array Recorder) has been developed to provide enhancements to existing field-deployable microphone phased arrays utilized for aeroacoustic flyover measurements of airframe and propulsive noise sources. The proposed system allows for the elimination of multiple miles of sensor wiring in an array installation, thereby improving the scalability of the overall system, increasing the fault-tolerance of the hardware, and reducing the effort needed to build-up and tear-down an array in the field. A demonstration of the NeAR concept was performed at Edwards Air Force Base in California in March – April, 2018, where twelve individual NeAR microphones were deployed as a piggyback on a conventional phased array system deployed for airframe noise flyover testing. The microphones operated successfully during the demonstration with good time history and spectral correlations shown between the NeAR units and conventional microphones located nearby in the array. The NeAR concept has spinoffs beyond its use for phased arrays, including applications in remote environmental sensing and noise monitoring.

Cull.iton, William G.↗
NASA NTRS2003

System Modeling and Diagnostics for Liquefying-Fuel Hybrid Rockets

A Hybrid Combustion Facility (HCF) was recently built at NASA Ames Research Center to study the combustion properties of a new fuel formulation that burns approximately three times faster than conventional hybrid fuels. Researchers at Ames working in the area of Integrated Vehicle Health Management recognized a good opportunity to apply IVHM techniques to a candidate technology for next generation launch systems. Five tools were selected to examine various IVHM techniques for the HCF. Three of the tools, TEAMS (Testability Engineering and Maintenance System), L2 (Livingstone2), and RODON, are model-based reasoning (or diagnostic) systems. Two other tools in this study, ICS (Interval Constraint Simulator) and IMS (Inductive Monitoring System) do not attempt to isolate the cause of the failure but may be used for fault detection. Models of varying scope and completeness were created, both qualitative and quantitative. In each of the models, the structure and behavior of the physical system are captured. In the qualitative models, the temporal aspects of the system behavior and the abstraction of sensor data are handled outside of the model and require the development of additional code. In the quantitative model, less extensive processing code is also necessary. Examples of fault diagnoses are given.

Poll, Scott↗
NASA NTRS2015

Towards Real-Time, On-Board, Hardware-Supported Sensor and Software Health Management for Unmanned Aerial Systems

For unmanned aerial systems (UAS) to be successfully deployed and integrated within the national airspace, it is imperative that they possess the capability to effectively complete their missions without compromising the safety of other aircraft, as well as persons and property on the ground. This necessity creates a natural requirement for UAS that can respond to uncertain environmental conditions and emergent failures in real-time, with robustness and resilience close enough to those of manned systems. We introduce a system that meets this requirement with the design of a real-time onboard system health management (SHM) capability to continuously monitor sensors, software, and hardware components. This system can detect and diagnose failures and violations of safety or performance rules during the flight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the-fly temporal and Bayesian probabilistic fault diagnosis; and (3) an unobtrusive, lightweight, read-only, low-power realization using Field Programmable Gate Arrays (FPGAs) that avoids overburdening limited computing resources or costly re-certification of flight software. We call this approach rt-R2U2, a name derived from its requirements. Our implementation provides a novel approach of combining modular building blocks, integrating responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. We demonstrate this approach using actual flight data from the NASA Swift UAS.

Unmanned Aerial System↗
NASA NTRS2012

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery

Space Propulsion↗
NASA NTRS2010

Shifting the Inertial Navigation Paradigm with MEMS Technology

Why don t you use MEMS? is one of the most common questions posed to navigation systems engineers designing inertial navigation solutions in the modern era. The question stems from a general understanding that great strides have been made in terrestrial MEMS accelerometers and attitude rate sensors in terms of accuracy, mass, and power. Yet, when compared on a unit-to-unit basis, MEMS devices do not provide comparable performance (accuracy) to navigation grade sensors. This paper will propose a paradigm shift where the comparison in performance is between multiple MEMS devices and a single navigation grade sensor. The concept is that systematically, a sufficient number of MEMS sensors may mathematically provide comparable performance to a single navigation grade device and be competitive in terms power and mass allocations when viewed on a systems level. The implication is that both inertial navigation system design and fault detection, identification, and recovery could benefit from a system of MEMS devices in the same way that swarm sensing has benefited Earth observation and astronomy. A survey of the state of the art in inertial sensor accuracy scaled by mass and power will be provided to show the specific error in MEMS and navigation graded devices, a mathematical comparison of multi-unit to single-unit sensor errors will be developed, and preliminary applications to Constellation vehicles will be explored.

Crain, Timothy↗
NASA NTRS2010

Shifting the Intertial Navigation Paradigm with the MEMS Technology

"Why don't you use MEMS?" is of the most common questions posed to navigation systems engineers designing inertial navigation solutions in the modern era. The question stems from a general understanding that great strides have been made in terrestrial MEMS accelerometers and attitude rate sensors in terms of accuracy, mass, and power. Yet, when compared on a unit-to-unit basis, MEMS devices do not provide comparable performance (accuracy) to navigation grade sensors in several key metrics. This paper will propose a paradigm shift where the comparison in performance is between multiple MEMS devices and a single navigation grade sensor. The concept is that systematically, a sufficient number of MEMS sensors may mathematically provide comparable performance to a single navigation grade device and be competitive in terms power and mass allocations when viewed on a systems level. The implication is that both inertial navigation system design and fault detection, identification, and recovery could benefit from a system of MEMS devices in the same way that swarm sensing has benefited Earth observation and astronomy. A survey of the state of the art in inertial sensor accuracy scaled by mass and power will be provided to show the scaled error in MEMS and navigation graded devices, a mathematical comparison of multi-unit to single-unit sensor errors will be developed, and preliminary application to an Orion lunar skip atmospheric entry trajectory will be explored.

Crain, Timothy P., II↗
NASA NTRS2000

On-Board Real-Time State and Fault Identification for Rovers

For extended autonomous operation, rovers must identify potential faults to determine whether its execution needs to be halted or not. At the same time, rovers present particular challenges for state estimation techniques: they are subject to environmental influences that affect senior readings during normal and anomalous operation, and the sensors fluctuate rapidly both because of noise and because of the dynamics of the rover's interaction with its environment. This paper presents MAKSI, an on-board method for state estimation and fault diagnosis that is particularly appropriate for rovers. The method is based on a combination of continuous state estimation, wing Kalman filters, and discrete state estimation, wing a Markov-model representation.

Washington, Richard↗