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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives

Abstract Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.

25 ENERGY STORAGE↗
DOE OSTI2023

Battery state-of-health diagnostics during fast cycling using physics-informed deep-learning

Rapid, in-situ Li-ion battery state-of-health (SOH) quantification is challenging. Li-ion battery aging can vary significantly with chemistry, operating conditions, cycling demands, electrode design, and operation history. As a cell ages, optimal and safe operating conditions need to be adapted to account for battery degradation by tracking critical aging modes such as loss-of-lithium-inventory (LLI), loss-of-active-material (LAM) in either electrode, and/or impedance rise. This manuscript describes a framework for identifying battery aging modes in-operando using fast-rate voltage charge/discharge responses. The framework uses a physically based Li-ion battery model to produce synthetic high-rate responses at aged states. The aging model is calibrated against experimental data from cells with different electrode loadings and cycled under a variety of fast-charging conditions (1 h, 15 min, 10 min, and 7 min charging). The synthetically generated high-rate responses at aged states are then used to train a deep-learning model to identify real cell state-of-health from fast charge/discharge battery voltage responses. The synthetically trained deep-learning model performance is validated by comparing to standard incremental capacity analysis and half-cell measurements. Finally, the framework demonstrates the benefits of using high-rate physics-based models to generate synthetic data for training deep-learning models.

25 ENERGY STORAGE↗
NASA NTRS2002

Non-Invasive Health Diagnostics using Eye as a 'Window to the Body'

As a 'window to the body', the eye offers the opportunity to use light in various forms to detect ocular and systemic abnormalities long before clinical symptoms appear and help develop preventative/therapeutic countermeasures early. The effects of space travel on human body are similar to those of normal aging. For example, radiation exposure in space could lead to formation of cataracts and cancer by damaging the DNA and causing gene mutation. Additionally, the zero-gravity environment causes fluid shifts in the upper extremities of the body and changes the way blood flows and organ system performs. Here on Earth, cataract, age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are major eye diseases and are expected to double in next two decades. To detect, prevent, and treat untoward effects of prolonged space travel in real-time requires the development of non-invasive diagnostic technologies that are compact and powerful. We are developing fiber-optic sensors to evaluate the ocular tissues in health, aging, and disease employing the techniques of dynamic light scattering (cataract, uveitis, Alzheimer's, glaucoma, DR, radiation damage, refractive surgery outcomes), auto-fluorescence (aging, DR), laser-Doppler flowmetry (choroidal blood flow), Raman spectroscopy (AMD), polarimetry (diabetes), and retinal oximetry (occult blood loss). The non-invasive feature of these technologies integrated in a head-mounted/goggles-like device permits frequent repetition of tests, enabling evaluation of the results to therapy that may ultimately be useful in various telemedicine applications on Earth and in space.

Ansari, Rafat R.↗
NASA NTRS2012

Rotorcraft Diagnostics

Health management (HM) in any engineering systems requires adequate understanding about the system s functioning; a sufficient amount of monitored data; the capability to extract, analyze, and collate information; and the capability to combine understanding and information for HM-related estimation and decision-making. Rotorcraft systems are, in general, highly complex. Obtaining adequate understanding about functioning of such systems is quite difficult, because of the proprietary (restricted access) nature of their designs and dynamic models. Development of an EIM (exact inverse map) solution for rotorcraft requires a process that can overcome the abovementioned difficulties and maximally utilize monitored information for HM facilitation via employing advanced analytic techniques. The goal was to develop a versatile HM solution for rotorcraft for facilitation of the Condition Based Maintenance Plus (CBM+) capabilities. The effort was geared towards developing analytic and reasoning techniques, and proving the ability to embed the required capabilities on a rotorcraft platform, paving the way for implementing the solution on an aircraft-level system for consolidation and reporting. The solution for rotorcraft can he used offboard or embedded directly onto a rotorcraft system. The envisioned solution utilizes available monitored and archived data for real-time fault detection and identification, failure precursor identification, and offline fault detection and diagnostics, health condition forecasting, optimal guided troubleshooting, and maintenance decision support. A variant of the onboard version is a self-contained hardware and software (HW+SW) package that can be embedded on rotorcraft systems. The HM solution comprises components that gather/ingest data and information, perform information/feature extraction, analyze information in conjunction with the dependency/diagnostic model of the target system, facilitate optimal guided troubleshooting, and offer decision support for optimal maintenance.

Haste, Deepak↗
NASA NTRS1992

Rocket engine exhaust plume diagnostics and health monitoring/management during ground testing

The current status of a rocket exhaust plume diagnostics program sponsored by NASA is reviewed. The near-term objective of the program is to enhance test operation efficiency and to provide for safe cutoff of rocket engines prior to incipient failure, thereby avoiding the destruction of the engine and the test complex and preventing delays in the national space program. NASA programs that will benefit from the nonintrusive remote sensed rocket plume diagnostics and related vehicle health management and nonintrusive measurement program are Space Shuttle Main Engine, National Launch System, National Aero-Space Plane, Space Exploration Initiative, Advanced Solid Rocket Motor, and Space Station Freedom. The role of emission spectrometry and other types of remote sensing in rocket plume diagnostics is discussed.

Chenevert, D. J.↗
NASA NTRS2004

Development of an Information Fusion System for Engine Diagnostics and Health Management

Aircraft gas-turbine engine data are available from a variety of sources including on-board sensor measurements, maintenance histories, and component models. An ultimate goal of Propulsion Health Management (PHM) is to maximize the amount of meaningful information that can be extracted from disparate data sources to obtain comprehensive diagnostic and prognostic knowledge regarding the health of the engine. Data Fusion is the integration of data or information from multiple sources, to achieve improved accuracy and more specific inferences than can be obtained from the use of a single sensor alone. The basic tenet underlying the data/information fusion concept is to leverage all available information to enhance diagnostic visibility, increase diagnostic reliability and reduce the number of diagnostic false alarms. This paper describes a basic PHM Data Fusion architecture being developed in alignment with the NASA C17 Propulsion Health Management (PHM) Flight Test program. The challenge of how to maximize the meaningful information extracted from disparate data sources to obtain enhanced diagnostic and prognostic information regarding the health and condition of the engine is the primary goal of this endeavor. To address this challenge, NASA Glenn Research Center (GRC), NASA Dryden Flight Research Center (DFRC) and Pratt & Whitney (P&W) have formed a team with several small innovative technology companies to plan and conduct a research project in the area of data fusion as applied to PHM. Methodologies being developed and evaluated have been drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation, and fuzzy logic. This paper will provide a broad overview of this work, discuss some of the methodologies employed and give some illustrative examples.

Volponi, Allan J.↗
NASA NTRS2001

Health Monitoring Technology for Thermal Protection Systems on Reusable Hypersonic Vehicles

Integrated subsystem health diagnostics is an area where major improvements have been identified for potential implementation into the design of new reusable launch vehicles (RLVs) in order to reduce life cycle costs, to increase safety margins, and to improve mission reliability. This talk summarizes a joint effort between NASA Ames and industry partners to develop rapid non-contact diagnostic tools for health and performance monitoring of thermal protection systems (TPS) on future RLVs. The specific goals for TPS health monitoring are to increase the speed and reliability of TPS inspections for improved operability at lower cost. The technology being developed includes a 3-D laser scanner for examining the exterior surface of the TPS, and a subsurface microsensor suite for monitoring the health and performance of the TPS. The sensor suite consists of passive overlimit sensors and sensors for continuous parameter monitoring in flight. The sensors are integrated with radio-frequency identification (RFID) microchips to enable wireless communication of-the sensor data to an external reader that may be a hand-held scanner or a large portal. Prototypes of the laser system and both types of subsurface sensors have been developed. The laser scanner was tested on Shuttle Orbiter Columbia and was able to dimension surface chips and holes on a variety of TPS materials. The temperature-overlimit microsensor has a diameter under 0.05 inch (suitable for placement in gaps between ceramic TPS tiles) and can withstand 700 F for 15 minutes.

Milos, Frank S.↗
DOE OSTI2024

Rapid Electrochemical Diagnosis of Battery Health and Safety from Cells to Modules

Rapid electrochemical diagnosis of battery health and failure is critical for ensuring reliable battery performance and battery safety. Traditional battery health diagnostics such as capacity measurements and DC pulse tests are reliable and well-understood, however, these measurements of battery capacity and resistance do not capture all aspects of battery degradation. Other aspects of degradation, such as electrolyte decomposition, lithium-plating, and particle cracking are difficult to detect electrochemically but are crucial to measure to get a full picture of battery safety and flag out potential failures. In this work, lab- and field-aged commercial lithium-ion batteries and modules of various chemistries and formats are tested using a variety of traditional electrochemical characterization methods as well as using 2-minute pseudo-random DC pulse sequences at rest and during charge/discharge. The electrochemical measurements are compared to physical cell measurements, cell efficiency, drive cycle performance, physical and thermal heterogeneity, and qualitative safety metrics using statistical and machine-learning methods to discover if a comprehensive "battery health map" can be accurately identified using only rapid DC measurements.

ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗
NASA NTRS2000

Development of Wireless Subsurface Microsensors for Health Monitoring of Thermal Protection Systems

Low cost access to space is a primary goal for both NASA and the U.S. aerospace industry. Integrated subsystem health diagnostics is an area where major improvements have been identified for potential implementation into the design of new reusable launch vehicles (RLVS) in order to reduce life cycle costs, increase safety margins and improve mission reliability. A number of efforts are underway to use existing and emerging technologies to establish new methods for vehicle health monitoring on operational vehicles as well as X-vehicles. This paper summarizes a joint effort between several NASA centers and industry partners to develop rapid wireless diagnostic tools for failure management and long-term TPS performance monitoring of thermal protection systems (TPS) on future RLVS. An embedded wireless microsensor suite is being designed to allow rapid subsurface TPS health monitoring and damage assessment. This sensor suite will consist of both passive overlimit sensors and sensors for continuous parameter monitoring in flight. The on-board diagnostic system can be used to radio in maintenance requirements before landing and the data could also be used to assist in design validation for X-vehicles. For a 3rd generation vehicle, wireless diagnostics should be at a stage of technical development that will allow use for intelligent feedback systems for guidance and navigation control applications and can also serve as feedback for TPS that can intelligently adapt to its environment.

Pallix, Joan↗
NASA NTRS2010

NASA Human Health and Performance Center (NHHPC)

The NASA Human Health and Performance Center (NHHPC) will provide a collaborative and virtual forum to integrate all disciplines of the human system to address spaceflight, aviation, and terrestrial human health and performance topics and issues. The NHHPC will serve a vital role as integrator, convening members to share information and capture a diverse knowledge base, while allowing the parties to collaborate to address the most important human health and performance topics of interest to members. The Center and its member organizations will address high-priority risk reduction strategies, including research and technology development, improved medical and environmental health diagnostics and therapeutics, and state-of-the art design approaches for human factors and habitability. Once full established in 2011, the NHHPC will focus on a number of collaborative projects focused on human health and performance, including workshops, education and outreach, information sharing and knowledge management, and research and technology development projects, to advance the study of the human system for spaceflight and other national and international priorities.

Davis, J. R.↗
NASA NTRS2001

Wireless Subsurface Microsensors for Health Monitoring of Thermal Protection Systems on Hypersonic Vehicles

Health diagnostics is an area where major improvements have been identified for potential implementation into the design of new reusable launch vehicles in order to reduce life cycle costs, to increase safety margins, and to improve mission reliability. NASA Ames is leading the effort to develop inspection and health management technologies for thermal protection systems. This paper summarizes a joint project between NASA Ames and SRI International to develop 'SensorTags,' radio frequency identification devices coupled with event-recording sensors, that can be embedded in the thermal protection system to monitor temperature or other quantities of interest. Two prototype SensorTag designs containing thermal fuses to indicate a temperature overlimit are presented and discussed.

Milos, Frank S.↗
NASA NTRS2001

Wireless Subsurface Sensors for Health Monitoring of Thermal Protection Systems on Hypersonic Vehicles

Health diagnostics is an area where major improvements have been identified for potential implementation into the design of new reusable launch vehicles (RLVs) in order to reduce life cycle costs, to increase safety margins, and to improve mission reliability. NASA Ames is leading the effort to develop inspection and health management technologies for thermal protection systems. This paper summarizes a joint project between NASA Ames and industry partners to develop "wireless" devices that can be embedded in the thermal protection system to monitor temperature or other quantities of interest. These devices are sensors integrated with radio-frequency identification (RFID) microchips to enable non-contact communication of sensor data to an external reader that may be a hand-held scanner or a large portal. Both passive and active prototype devices have been developed. The passive device uses a thermal fuse to indicate the occurrence of excessive temperature. This device has a diameter under 0.13 cm. (suitable for placement in gaps between ceramic TPS tiles on an RLV) and can withstand 370 C for 15 minutes. The active device contains a small battery to provide power to a thermocouple for recording a temperature history during flight. The bulk of the device must be placed beneath the TPS for protection from high temperature, but the thermocouple can be placed in a hot location such as near the external surface.

Milos, Frank S.↗
NASA NTRS2002

Active Wireless Temperature Sensors for Aerospace Thermal Protection Systems

Health diagnostics is an area where major improvements have been identified for potential implementation into the design of new reusable launch vehicles in order to reduce life-cycle costs, to increase safety margins, and to improve mission reliability. NASA Ames is leading the effort to advance inspection and health management technologies for thermal protection systems. This paper summarizes a joint project between NASA Ames and Korteks to develop active wireless sensors that can be embedded in the thermal protection system to monitor sub-surface temperature histories. These devices are thermocouples integrated with radio-frequency identification circuitry to enable acquisition and non-contact communication of temperature data through aerospace thermal protection materials. Two generations of prototype sensors are discussed. The advanced prototype collects data from three type-k thermocouples attached to a 2.54-cm square integrated circuit.

Milos, Frank S.↗
DOE OSTI2023

Machine-Learning for Battery Health Diagnosis

Battery health diagnosis often requires time-consuming measurements in laboratory conditions, but these types of measurements are unsuitable for use in real-world application such as electric vehicles. Instead, rapid measurements that can be conducted at varying environmental conditions need to be used to monitor battery health. Machine-learning techniques can then be utilized to connect these rapid measurements to health diagnostic information recorded in the lab. This presentation is a part of a tutorial on using machine-learning methods to analyze and predict battery state.

battery↗
NASA NTRS1996

Quantification of Gear Tooth Damage by Optimal Tracking of Vibration Signatures

This paper presents a technique for quantifying the wear or damage of gear teeth in a transmission system. The procedure developed in this study can be applied as a part of either an onboard machine health-monitoring system or a health diagnostic system used during regular maintenance. As the developed methodology is based on analysis of gearbox vibration under normal operating conditions, no shutdown or special modification of operating parameters is required during the diagnostic process. The process of quantifying the wear or damage of gear teeth requires a set of measured vibration data and a model of the gear mesh dynamics. An optimization problem is formulated to determine the profile of a time-varying mesh stiffness parameter for which the model output approximates the measured data. The resulting stiffness profile is then related to the level of gear tooth wear or damage. The procedure was applied to a data set generated artificially and to another obtained experimentally from a spiral bevel gear test rig. The results demonstrate the utility of the procedure as part of an overall health-monitoring system.

Choy, F. K.↗