Statistical Reliability Engineering, Pham

Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman
Название: The Elements of Statistical Learning
ISBN: 0387848576 ISBN-13(EAN): 9780387848570
Издательство: Springer
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Цена: 69870.00 T
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Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.

Автор: Pham
Название: Applications in Reliability and Statistical Computing
ISBN: 3031212312 ISBN-13(EAN): 9783031212314
Издательство: Springer
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Цена: 158380.00 T
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Описание: This book discusses practical applications of reliability and statistical methods and techniques in various disciplines, using machine learning, artificial intelligence, optimization, and other computation methods. Bringing together research from international experts, each chapter aims to cover both methods and practical aspects on reliability or statistical computations with emphasis on applications. 5G and IoT are set to generate an estimated 1 billion terabytes of data by 2025 and companies continue to search for new techniques and tools that can help them practice data collection effectively in promoting their business. This book explores the era of big data through reliability and statistical computing, showcasing how almost all applications in our daily life have experienced a dramatic shift in the past two decades to a truly global industry. Including numerous illustrations and worked examples, the book is of interest to researchers, practicing engineers, and postgraduate students in the fields of reliability engineering, statistical computing, and machine learning.

Автор: Serkan Eryilmaz
Название: Discrete Stochastic Models and Applications for Reliability Engineering and Statistical Quality Control
ISBN: 0367749831 ISBN-13(EAN): 9780367749835
Издательство: Taylor&Francis
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Цена: 148010.00 T
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Описание: This book provides real-life examples and illustrations of models in reliability engineering and statistical quality control and establishes a connection between the theoretical framework and their engineering applications.

Автор: S. Trianntafyllou, Ioanni
Название: Statistical Modeling of Reliability Structures and Industrial Processes
ISBN: 1032066253 ISBN-13(EAN): 9781032066257
Издательство: Taylor&Francis
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Цена: 132710.00 T
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Описание: Discusses advances in the field of reliability engineering with real-life applications. Covers consecutive-type reliability structures and applications in the optimization of telecommunication networks, and complex infrared detecting systems. Discusses probabilistic approaches and statistical methods to investigate the performance and the design of reliability structures.

Provides simulation-based analyses and real data case studies related to reliability engineering.

Автор: Anatoly Lisnianski; Ilia Frenkel
Название: Recent Advances in System Reliability
ISBN: 1447126831 ISBN-13(EAN): 9781447126836
Издательство: Springer
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Цена: 156720.00 T
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Описание: This examination of developments in modern reliability theory, such as signatures, multi-state systems and statistical inference, describes the latest achievements in these fields, and details how they can be applied to reliability engineering practice.

Автор: Marvin Rausand
Название: System Reliability Theory: Models, Statistical Methods, and Applications, 2nd Edition
ISBN: 047147133X ISBN-13(EAN): 9780471471332
Издательство: Wiley
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Цена: 162630.00 T
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Описание: An examination of system reliability theory, this title features in-depth discussion of dependability management and reliability centered systems as well as functional safety issues-matters critical to the IEC standards.

Автор: Merton Hubbard
Название: Statistical Quality Control for the Food Industry
ISBN: 1489926798 ISBN-13(EAN): 9781489926791
Издательство: Springer
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Цена: 104480.00 T
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Описание: Considering the ability of food processing companies to consistently manu- facture safe foods with uniform quality over the past 20 or 30 years without these new tools and new systems, one might expect that quality control improvements would be marginal.

Автор: Nikolay Reshetov; Valery A. Svetlitsky
Название: Statistical Dynamics and Reliability Theory for Mechanical Structures
ISBN: 3540442979 ISBN-13(EAN): 9783540442974
Издательство: Springer
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Цена: 149060.00 T
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Описание: This textbook systematically presents the fundamentals of statistical dynamics and reliability theory and describes in detail the theory of Markovian processes used during the analysis of random dynamic processes in mechanical systems.

Автор: Nozer D. Singpurwalla; Simon P. Wilson
Название: Statistical Methods in Software Engineering
ISBN: 1461268206 ISBN-13(EAN): 9781461268208
Издательство: Springer
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Цена: 102480.00 T
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Описание: In establishing a framework for dealing with uncertainties in software engineering, and for using quantitative measures in related decision-making, this text puts into perspective the large body of work having statistical content that is relevant to software engineering.

Автор: Rekab, Kamel , Shaikh, Muzaffar
Название: Statistical Design of Experiments with Engineering Applications
ISBN: 0367393026 ISBN-13(EAN): 9780367393021
Издательство: Taylor&Francis
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Цена: 65320.00 T
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Описание:

In today's high-technology world, with flourishing e-business and intense competition at a global level, the search for the competitive advantage has become a crucial task of corporate executives. Quality, formerly considered a secondary expense, is now universally recognized as a necessary tool. Although many statistical methods are available for determining quality, there has been no guide to easy learning and implementation until now. Filling that gap, Statistical Design of Experiments with Engineering Applications, provides a ready made, quick and easy-to-learn approach for applying design of experiments techniques to problems. The book uses quality as the main theme to explain various design of experiments concepts.

The authors examine the entire product lifecycle and the tools and techniques necessary to measure quality at each stage. They explain topics such as optimization, Taguchi's method, variance reduction, and graphical applications based on statistical techniques. Wherever applicable the book supplies practical rules of thumb, step-wise procedures that allow you to grasp concepts quickly and apply them appropriately, and examples that demonstrate how to apply techniques. Emphasizing the importance of quality to products and services, the authors include concepts from the field of Quality Engineering. Written with an emphasis on application and not on bogging you down with the theoretical underpinnings, the book enables you to solve 80% of design problems without worrying about the derivation of mathematical formulas.

Автор: Stamatis, D. H.
Название: Essential Statistical Concepts for the Quality Professional
ISBN: 0367381427 ISBN-13(EAN): 9780367381424
Издательство: Taylor&Francis
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Цена: 67360.00 T
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Описание:

The essence of any root cause analysis in our modern quality thinking is to go beyond the actual problem. This means not only do we have to fix the problem at hand but we also have to identify why the failure occurred and what was the opportunity to apply the appropriate knowledge to avoid the problem in the future. Essential Statistical Concepts for the Quality Professional offers a new non-technical statistical approach to quality for effective improvement and productivity by focusing on very specific and fundamental methodologies and tools for the future.

Written by an expert with more than 30 years of experience in management, quality training, and consulting, the book examines the fundamentals of statistical understanding, and by doing so demonstrates the importance of using statistics in the decision making process. The author points out pitfalls to keep in mind when undertaking an experiment for improvement and explains how to use statistics in improvement endeavors. He discusses data interpretation, common tests and confidence intervals, and how to plan experiments for improvement. The book expands the notion of experimentation by dealing with mathematical models such as regression to optimize the improvement and understand the relationship between several factors. It emphasizes the need for sampling and introduces specific techniques to make sure accuracy and precision of the data is appropriate and applicable for the study at hand.

The author's approach is somewhat new and unique; however, he details tools and methodologies that can be used to evaluate the system for prevention. These tools and methodologies focus on structured, repeatable processes that can be instrumental in finding real, fixable causes of the human errors and equipment failures that lead to quality issues.

Автор: Bagdonavicius, Vilijandas , Nikulin, Mikhail
Название: Accelerated Life Models
ISBN: 0367396637 ISBN-13(EAN): 9780367396633
Издательство: Taylor&Francis
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Цена: 65320.00 T
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Описание:

The authors of this monograph have developed a large and important class of survival analysis models that generalize most of the existing models. In a unified, systematic presentation, this monograph fully details those models and explores areas of accelerated life testing usually only touched upon in the literature.

Accelerated Life Models: Modeling and Statistical Analysis presents models, methods of data collection, and statistical analysis for failure-time regression data in accelerated life testing and for degradation data with explanatory variables. In addition to the classical results, the authors devote considerable attention to models with time-varying explanatory variables and to methods of semiparametric estimation. They also examine the simultaneous analysis of degradation and failure-time data when the intensities of failure in different modes depend on the level of degradation and the values of explanatory variables.

The authors avoid technical details by explaining the ideas and referring to resources where thorough analysis can be found. Whether used for teaching, research or general reference, Accelerated Life Models: Modeling and Statistical Analysis provides new and known models and modern methods of accelerated life data analysis.