RENEW V3.2 User's Manual, Maintenance Estimation Simulation for Space Station Freedom Program PDF Download
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Author: Bruce L. Bream
Publisher:
ISBN:
Category :
Languages : en
Pages : 50
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Book Description
Author: Bruce L. Bream
Publisher:
ISBN:
Category :
Languages : en
Pages : 50
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Book Description
Author: National Aeronautics and Space Administration (NASA)
Publisher: Createspace Independent Publishing Platform
ISBN: 9781722731168
Category :
Languages : en
Pages : 48
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Book Description
RENEW is a maintenance event estimation simulation program developed in support of the Space Station Freedom Program (SSFP). This simulation uses reliability and maintainability (R&M) and logistics data to estimate both average and time dependent maintenance demands. The simulation uses Monte Carlo techniques to generate failure and repair times as a function of the R&M and logistics parameters. The estimates are generated for a single type of orbital replacement unit (ORU). The simulation has been in use by the SSFP Work Package 4 prime contractor, Rocketdyne, since January 1991. The RENEW simulation gives closer estimates of performance since it uses a time dependent approach and depicts more factors affecting ORU failure and repair than steady state average calculations. RENEW gives both average and time dependent demand values. Graphs of failures over the mission period and yearly failure occurrences are generated. The averages demand rate for the ORU over the mission period is also calculated. While RENEW displays the results in graphs, the results are also available in a data file for further use by spreadsheets or other programs. The process of using RENEW starts with keyboard entry of the R&M and operational data. Once entered, the data may be saved in a data file for later retrieval. The parameters may be viewed and changed after entry using RENEW. The simulation program runs the number of Monte Carlo simulations requested by the operator. Plots and tables of the results can be viewed on the screen or sent to a printer. The results of the simulation are saved along with the input data. Help screens are provided with each menu and data entry screen. Bream, Bruce L. Glenn Research Center ...
Author:
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Category : Government publications
Languages : en
Pages : 1638
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Author:
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Category : Aeronautics
Languages : en
Pages : 506
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Author:
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ISBN:
Category : Government publications
Languages : en
Pages : 854
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Author:
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ISBN:
Category : Aeronautics
Languages : en
Pages : 492
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Author:
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ISBN:
Category :
Languages : en
Pages : 1442
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Author:
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ISBN:
Category : Science
Languages : en
Pages : 772
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Author: Richard D. Johnson
Publisher:
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Category : Space colonies
Languages : en
Pages : 204
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Book Description
Author: Max Kuhn
Publisher: Springer Science & Business Media
ISBN: 1461468493
Category : Medical
Languages : en
Pages : 595
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Book Description
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
