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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
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Analysis of Regulatory Guidance for Health Monitoring

The purpose of this study was to assess the connection between current FAA regulations and the incorporation of Health Management (HM) systems into commercial aircraft. To address the overall objectives ARINC: (1) investigated FAA regulatory guidance, (2) investigated airline maintenance practices, (3) systematically identified regulations and practices that would be affected or could act as barriers to the introduction of HM technology, and (4) assessed regulatory and operational tradeoffs that should be considered for implementation. The assessment procedure was validated on a postulated structural HM capability for the B757 horizontal stabilizer.

Munns, Thomas E.↗
NASA NTRS2006

Valve Health Monitoring System Utilizing Smart Instrumentation

The valve monitoring system is a stand alone unit with network capabilities for integration into a higher level health management system. The system is designed for aiding in failure predictions of high-geared ball valves and linearly actuated valves. It performs data tracking and archiving for identifying degraded performance. The data collection types are cryogenic cycles, total cycles, inlet temperature, body temperature torsional strain, linear bonnet strain, preload position, total travel and total directional changes. Events are recorded and time stamped in accordance with the IRIG B True Time. The monitoring system is designed for use in a Class 1 Division II explosive environment. The basic configuration consists of several instrumentation sensor units and a base station. The sensor units are self contained microprocessor controlled and remotely mountable in three by three by two inches. Each unit is potted in a fire retardant substance without any cavities and limited to low operating power for maintaining safe operation in a hydrogen environment. The units are temperature monitored to safeguard against operation outside temperature limitations. Each contains 902-928 MHz band digital transmitters which meet Federal Communication Commission's requirements and are limited to a 35 foot transmission radius for preserving data security. The base-station controller correlates data from the sensor units and generates data event logs on a compact flash memory module for database uploading. The entries are also broadcast over an Ethernet network. Nitrogen purged National Electrical Manufactures Association (NEMA) Class 4 enclosures are used to house the base-station

Jensen, Scott L.↗
NASA NTRS2013

Optimized Radar Remote Sensing for Levee Health Monitoring

Radar remote sensing offers great potential for high resolution monitoring of ground surface changes over large areas at one time to detect movement on and near levees and for location of seepage through levees. Our NASA-funded projects to monitor levees in the Sacramento Delta and the Mississippi River have developed and demonstrated methods to use radar remote sensing to measure quantities relevant to levee health and of great value to emergency response. The DHS-funded project will enable us is to define how to optimally monitor levees in this new way and set the stage for transition to using satellite SAR (synthetic aperture radar) imaging for better temporal and spatial coverage at lower cost to the end users.

radar remote sensing↗
NASA NTRS2019

Prognostics and Health Monitoring in Li-Ion Batteries and Capacitors Using Physics-Based Modeling Approach

In order to tackle and solve the prediction problem of the lifetime of Li-ion batteries, it is essential to have awareness of the current state and health of the battery pack. To be able to accurately predict the future state of any system, one must possess knowledge of its current and future operations. Using derived models of the current and future system behavior, a model-based prognostics approach can be implemented as a solution to the prediction problem. As more and more autonomous electric vehicles progressively emerge in our daily life, a very critical challenge lies in accurate prediction of remaining useful life of the systems/subsystems. Batteries, power electronics conditioning systems, and motors are integrated to form the powertrain in electric vehicles; one of the most critical systems. In the case of electric aircrafts, computing remaining flying time is critical for safety, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastropheThis presentation covers a physics-based modeling approach implemented for case studies in capacitor and battery prognostics which are an integral part of an electrical powertrain system. The general approach of model-based prognostics will be examined as a potential solution for safety critical problems related to battery state of charge and state of health.

Physics based modeling↗
NASA NTRS2015

Damage Detection and Verification System (DDVS) for In-Situ Health Monitoring

Project presentation for Game Changing Program Smart Book Release. Detection and Verification System (DDVS) expands the Flat Surface Damage Detection System (FSDDS) sensory panels damage detection capabilities and includes an autonomous inspection capability utilizing cameras and dynamic computer vision algorithms to verify system health. Objectives of this formulation task are to establish the concept of operations, formulate the system requirements for a potential ISS flight experiment, and develop a preliminary design of an autonomous inspection capability system that will be demonstrated as a proof-of-concept ground based damage detection and inspection system.

Damage detection↗
NASA NTRS2005

Rotordynamic Analysis and Feasibility Study of a Disk Spin Test Facility for Rotor Health Monitoring

Recently, National Aeronautics and Space Administration (NASA) initiated a program to achieve the significant improvement in aviation safety. One of the technical challenges is the design and development of accelerated experiments that mimic critical damage cases encountered in engine components. The Nondestructive Evaluation (NDE) Group at the NASA Glenn Research Center (GRC) is currently addressing the goal concerning propulsion health management and the development of propulsion system specific technologies intended to detect potential failures prior to catastrophe. For this goal the unique disk spin simulation system was assembled at NASA GRC, which allows testing of rotors with the spinning speeds up to 10K RPM, and at the elevated temperature environment reaching 540 C (1000 F). It is anticipated that the facility can be employed for detection of Low Cycle Fatigue disk cracking and further High Cycle Fatigue blade vibration. The controlled crack growth studies at room and elevated temperatures can be conducted on the turbine wheels, and various NDE techniques can be integrated and assessed as in-situ damage monitoring tools. Critical rotating parts in advanced gas turbine engines such as turbine disks frequently operate at high temperature and stress for long periods of time. The integrity of these parts must be proven by non-destructive evaluation (NDE) during various machining steps ranging from forging blank to finished shape, and also during the systematic overhaul inspections. Conventional NDE methods, however, have unacceptable limits. Some of these techniques are time-consuming and inconvenient for service aircraft testing. Almost all of these techniques require that the vicinity of the damage is known in advance. These experimental techniques can provide only local information and no indication of the structural strength at a component and/or system level. The shortcomings of currently available NDE methods lead to the requirement of new damage detection techniques that can provide global information on the rotating components/system, and, in addition, they do not require direct human access to the operating system. During this period of research considerable effort was directed towards the further development of experimental facility and development of the vibration-based crack detection methodology for rotating disks and shafts. A collection of papers and reports were written to describe the results of this work. The attached captures that effort and represents the research output during the grant period.

Sawicki, Jerzy T.↗
NASA NTRS1993

Pattern classifier for health monitoring of helicopter gearboxes

The application of a newly developed diagnostic method to a helicopter gearbox is demonstrated. This method is a pattern classifier which uses a multi-valued influence matrix (MVIM) as its diagnostic model. The method benefits from a fast learning algorithm, based on error feedback, that enables it to estimate gearbox health from a small set of measurement-fault data. The MVIM method can also assess the diagnosability of the system and variability of the fault signatures as the basis to improve fault signatures. This method was tested on vibration signals reflecting various faults in an OH-58A main rotor transmission gearbox. The vibration signals were then digitized and processed by a vibration signal analyzer to enhance and extract various features of the vibration data. The parameters obtained from this analyzer were utilized to train and test the performance of the MVIM method in both detection and diagnosis. The results indicate that the MVIM method provided excellent detection results when the full range of faults effects on the measurements were included in training, and it had a correct diagnostic rate of 95 percent when the faults were included in training.

Chin, Hsinyung↗
NASA NTRS2018

Health Monitoring and Prognostics for More Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining flying time/distance (for aircraft). This information is important, particularly in the case of unmanned vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing remaining flying time is also safety-critical, since an aircraft that runs out of battery charge while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem.For electric aircraft, propulsion is based on power generated from batteries. Thus, it is critical to monitor battery state charge and to estimate the ability of the battery to support mission activities as it is being discharged during flight operation. The ability of the vehicle to complete its given mission very much depends on the charge left in the batteries based on its operational route, maneuvering, weather conditions along with aging health of the batteries. Hence, for the purpose this discussion, consider the scenario of an unmanned electric aircraft that has some planned sequence of waypoints to reach throughout its mission. In such a scenario, for this particular aircraft, and within the region it is flown, at most two minutes are required to safely land the aircraft. Thus, it is desired to predict at which point in time the aircraft must begin to head to the runway and land.

Goebel, Kai↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Battery Prognostics↗
NASA NTRS2011

Investigation of Data Fusion Applied to Health Monitoring of Wind Turbine Drive train Components

The research described was performed on diagnostic tools used to detect damage to dynamic mechanical components in a wind turbine gearbox. Different monitoring technologies were evaluated by collecting vibration and oil debris data from tests performed on a "healthy" gearbox and a damaged gearbox in a dynamometer test stand located at the National Renewable Energy Laboratory. The damaged gearbox tested was removed from the field after experiencing component damage due to two losses of oil events and was retested under controlled conditions in the dynamometer test stand. Preliminary results indicate oil debris and vibration can be integrated to assess the health of the wind turbine gearbox.

Dempsey, Paula J.↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously approved and published information.

Systems Health Managent↗
DOE OSTI2024

Live State of Health Monitoring of Inverter Subsystems

This presentation talks about different failure modes and corresponding detection schemes of PV panels, PV inverters, electric machines such as motors and power converter. Several novel techniques have been presented that are capable of measuring degradation as well as detecting faults in various location in a PV based power system.

ENGINEERING,SOLAR ENERGY↗
NASA NTRS2019

Prognostics and Health Monitoring: Application to Electric Vehicles

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving-time distance (for cars) or flying time-distance (for aircraft). This information is important, particularly in the case of unmanned vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.

Electric Vehicles↗
NASA NTRS2010

Improving Bone-Health Monitoring in Astronauts: Recommended Use of Quantitative Computed Tomography [QCT] for Clinical and Operational Decisions by NASA

DXA measurement of areal bone mineral density [aBMD,g/cm2] is required by NASA for assessing skeletal integrity in astronauts. Due to the abundance of population-based data that correlate hip and spine BMDs to fragility fractures, BMD is widely applied as a predictor of fractures in the general aging population. In contrast, QCT is primarily a research technology that measures three-dimensional , volumetric BMD (vBMD,mg/cm3) of bone and is therefore capable of differentiating between cortical and trabecular components. Additionally, when combined with Finite Element Modeling [FEM], a computational tool, QCT data can be used to estimate the whole bone strength of the hip [FE strength] for a specific load vector. A recent report demonstrated that aBMD failed to correlate with incurred changes in FE strength (for fall and stance loading) by astronauts over typical 180-day ISS (International Space Station) missions. While there are no current guidelines for using QCT data in clinical practice, QCT increases the understanding of how bone structure and mineral content are affected by spaceflight and recovery on Earth. In order to understand/promote/consider the use of QCT, NASA convened a panel of clinicians specializing in osteoporosis. After reviewing the available, albeit limited, medical and research information from long-duration astronauts (e.g., data from DXA, QCT, FEM, biochemistry analyses, medical records and in-flight exercise performance) the panelists were charged with recommending how current and future research data and analyses could inform clinical and operational decisions. The Panel recommended that clinical bone tests on astronauts should include QCT (hip and lumbar spine) for occupational risk surveillance and for the estimation of whole hip bone strength as derived by FEM. FE strength will provide an improved index that NASA could use to select astronauts of optimal bone health for extended duration missions, for repeat missions or for specific mission operations.

Sibonga, J. D.↗
NASA NTRSDate not supplied

Systems Health Monitoring: Integrating FMEA into Bayesian Networks

The foreseeable high traffic density suggests that a large number of electric propulsion systems will enter the airspace, and that they will also operate at high frequency, e.g., large number of take offs and landings per unit time. The reliability of such critical systems is therefore key to ensure high safety standards in the low-altitude airspace. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures or lower-than-expected reliability by performing early fault detection by monitoring the systems. A key element of fault diagnosis is fault detection and isolation (FDI), which complexity increases with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and the number of sensors available. The proposed approach leverages combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework to aid FDI. Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. The integrated framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor signals. In this work, sub systems of Urban Air Mobility (UAM) type vehicle like avionics, structures, power-train etc. are taken into account to show the approach at the system level. This work integrates early design phase in the development of UAM type vehicles with diagnostic tools, which are often developed later in the product life-cycle, or retrofitted at a later time on systems. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).

UAM↗
DOE OSTI2023

Aerosol-Jet Printed Sensors for Environmental, Safety, and Health Monitoring: A Review

An emergent direct-write approach, aerosol-jet printing (AJP), is gaining attention for the deployment of rapid and affordable microadditively manufactured energy-efficient sensors and printed electronics. AJP enables a broad range of ink viscosities (0.001–1 Pa s) for printing diverse materials ranging from ceramics and metals to polymers and biological matter. Reproducible, high-spatial-resolution features (≈10 µm), and wide standoff distances (1–11 mm) between the nozzle and the substrate facilitate conformal printing of complex geometrical designs on nonplanar—e.g., stepped or curved—surfaces. Here this paper aims to provide a comprehensive overview of state-of-the-art AJP-based sensors (e.g., strain and temperature gauges, biosensors, photosensors, humidity and surface acoustic wave sensors, dielectric elastomer actuators, and motion, smoke, and hazardous gas detectors) and to discuss prospective applications. The drive toward cost-effective devices that are smaller, lighter, and better-performing remains a frontier challenge in the field of printed electronics. Consequently, as AJP becomes increasingly utilized in the high-volume manufacturing of miniaturized active and passive sensors, it opens a pathway for facile large-scale fabrication of devices for a wide range of consumer and industrial applications, including transportation, agriculture, infrastructure, aerospace, national defense, and healthcare.

3-dimensional↗
DOE OSTI2022

A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring

Automating the task of fault detection and diagnosis is crucial in the effort to reduce the operation and maintenance cost in the nuclear industry. This paper describes a physics-based approach for system-level diagnosis in thermal-hydraulic systems in nuclear power plants. The inclusion of physics information allows for the creation of virtual sensors, which provide improved fault diagnosis capability. The physics information also serves to better constrain diagnostic solutions to the physical domain. As a demonstration, various test cases for fault diagnosis in a high-pressure feedwater system were considered. The use of virtual sensors allows constructing performance models for two first-point feedwater heaters which would not have been possible otherwise due to the limited sensor set. Real-time plant data provided by a utility partner were used to assess the diagnostic approach. The detection of an abnormal event immediate after a plant startup pointed to faulty behaviors in the two first-point feedwater heaters. Further, this double-blind fault diagnosis was subsequently confirmed by the plant operator. In addition, several simulated sensor fault events demonstrated the capability of our algorithms in detecting and discriminating sensor faults.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗