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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

NASA-STD 3001 and the Human Integration Design Handbook (HIDH): Evolution of NASA-STD-3000

The Habitability & Environmental Factors and Space Medicine Divisions have developed the Space Flight Human System Standard (SFHSS) (NASA-STD-3001) to replace NASA-STD-3000 as a new NASA standard for all human spaceflight programs. The SFHSS is composed of 2 volumes. Volume 1, Crew Health, contains medical levels of care, permissible exposure limits, and fitness for duty criteria, and permissible outcome limits as a means of defining successful operating criteria for the human system. Volume 2, Habitability and Environmental Health, contains environmental, habitability and human factors standards. Development of the Human Integration Design Handbook (HIDH), a companion to the standard, is currently under construction and entails the update and revision of NASA-STD-3000 data. This new handbook will, in the fashion of NASA STD-3000, assist engineers and designers in appropriately applying habitability, environmental and human factors principles to spacecraft design. Organized in a chapter-module-element structure, the HIDH will provide the guidance for the development of requirements, design considerations, lessons learned, example solutions, background research, and assist in the identification of gaps and research needs in the disciplines. Subject matter experts have been and continue to be solicited to participate in the update of the chapters. The purpose is to build the HIDH with the best and latest data, and provide a broad representation from experts in industry, academia, the military and the space program. The handbook and the two standards volumes work together in a unique way to achieve the required level of human-system interface. All new NASA programs will be required to meet Volumes 1 and 2. Volume 2 presents human interface goals in broad, non-verifiable standards. Volume 2 also requires that each new development program prepare a set of program-specific human factors requirements. These program-specific human and environmental factors requirements must be verifiable and tailored to assure the new system meets the Volume 2 standards. Programs will use the HIDH to write their verifiable program-specific requirements.

Pickett, Lynn↗
NASA NTRS2009

Overview of Human Factors and Habitability at NASA

This slide presentation reviews the ongoing work on human factors and habitability in the development of the Constellation Program. The focus of the work is on how equipment, spacecraft design, tools, procedures and nutrition be used to improve the health, safety and efficiency of the crewmembers. There are slides showing the components of the Constellation Program, and the conceptual designs of the Orion Crew module, the lunar lander, (i.e., Altair) the microgravity EVA suit, and the lunar surface EVA suit, the lunar rover, and the lunar surface system infrastructure.

Connolly, Janis↗
DOE OSTI2021

Machine Learning for Gearbox Fault Prediction by Using Both Scada and Modeled Data

This presentation outlines the work in the paper titled "Prognostics of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data" published by the PHM Society and presented at its 2020 annual conference. It is accessible at https://papers.phmsociety.org/index.php/phmconf/article/download/1292/862. The technical work is on machine learning approaches for prognostics for gearbox faults. The methodology combines SCADA time series data and physics domain modeling data, derived from the models developed by the NREL team, as inputs to machine learning models to predict gearbox bearing failures with one month lead time. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score, and AUC (area under receiver operating characteristic curve). Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision, and F1 score, and AUC is about 33%, and 12%, and 6% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2022

Investigation of Multiple Data Streams for Gearbox Bearing Fault Prediction Through Machine-Learning Models

Operations and maintenance (O&M) cost of wind plant accounts up to 30% of total energy cost, which can be reduced through continuous monitoring and successfully detecting incipient wind turbine failures. To accomplish this, condition monitoring and predictive maintenance systems are being implemented in wind industry to support O&M decision making. A wide range of approaches for condition monitoring and fault prediction have been developed. These approaches generally use historical data of wind turbines collected by Supervisory Control and Data Acquisition (SCADA) system to identify patterns that lead to failure. These SCADA data show the overall condition of a wind turbine and can be leveraged to detect when the turbine's performance is degrading and to identify if a fault is developing. However, it becomes challenging to predict the failure of a specific wind turbine gearbox bearing, because the SCADA data are often not directly linked to the component. To bridge the gap, we have investigated features calculated from SCADA data using physics-based models and the gearbox design over the years. The damaged metric we used in the physics domain is frictional energy. Combining these physics domain variables with SCADA data as inputs to various machine learning models for gearbox bearing fault prediction, we have demonstrated the benefits of leveraging both physics and data domain models. It was an attempt to improve frictional-energy-based damage metric by adding data domain inputs, as we had learned that the frictional-energy-based damage metric alone is not sufficient to single out failed bearings from healthy. As condition monitoring data (either vibration or oil debris data) has become available at more and more wind plants, we would like to evaluate whether by adding the condition monitoring data can help further improve the performance of frictional-energy-based damage metric for gearbox bearing fault prediction. Both cases by modeling through various machine learning algorithms are discussed in this study along with some observations.

fault prediction↗
DOE OSTI2023

Wind Turbine Drivetrain Reliability Research - Gearbox Bearing Axial Cracking Failure Mode Example

The U.S. Department of Energy's National Renewable Energy Laboratory and Argonne National Laboratory have been conducting wind turbine drivetrain (formerly gearbox) reliability research for many years. Although the drivetrain focus has not changed, detailed projects are adjusted every few years based on dynamic needs seen in the field across the wind industry. This webinar will walk through the research methodology by using wind turbine gearbox bearing axial cracking failure mode as an example. The detailed steps include: 1) top failure mode identification based on actual failure data collected from project partners, 2) bench-top testing to identify possible contributing factors and formulate a damage metric, 3) physics domain modeling and validation through testing, 4) reliability assessment and prognosis based on the physics domain model and data domain inputs, and further enhancement through machine learning algorithms, using actual wind plant operational and failure event data. Hopefully, the presented work is of interest to the IISE community, and some members can apply their expertise to wind turbine and plant applications, helping enhance wind power generation technology advancement and its broader deployment.

axial cracking↗