Statistical Reliability Engineering

Methods, Models and Applications
 
 
Springer (Verlag)
  • erscheint ca. am 31. Oktober 2021
 
  • Buch
  • |
  • Hardcover
  • |
  • XXV, 350 Seiten
978-3-030-76903-1 (ISBN)
 
This book presents the state-of-the-art methodology and detailed analytical models and methods used to assess the reliability of complex systems and related applications in statistical reliability engineering. It is a textbook based mainly on the author's recent research and publications as well as experience of over 30 years in this field.

The book covers a wide range of methods and models in reliability, and their applications, including:

statistical methods and model selection for machine learning; models for maintenance and software reliability; statistical reliability estimation of complex systems; and statistical reliability analysis of k out of n systems, standby systems and repairable systems. Offering numerous examples and solved problems within each chapter, this comprehensive text provides an introduction to reliability engineering graduate students, a reference for data scientists and reliability engineers, and a thorough guide for researchers and instructors in the field.
1st ed. 2022
  • Englisch
  • Cham
  • |
  • Schweiz
Springer International Publishing
  • Für Beruf und Forschung
  • 50
  • |
  • 50 farbige Abbildungen, 50 s/w Abbildungen, 50 farbige Tabellen
  • |
  • 50 Tables, color; 50 Illustrations, color; 50 Illustrations, black and white; XXV, 350 p. 100 illus., 50 illus. in color.
  • Höhe: 23.5 cm
  • |
  • Breite: 15.5 cm
978-3-030-76903-1 (9783030769031)
10.1007/978-3-030-76904-8
weitere Ausgaben werden ermittelt

Dr. Hoang Pham is Distinguished Professor and former Chairman (2007-2013) of the Department of Industrial and Systems Engineering at Rutgers University, New Jersey. Before joining Rutgers, he was Senior Engineering Specialist with the Boeing Company and the Idaho National Engineering Laboratory. He has been served as Editor-in-Chief, Editor, Associate Editor, Guest Editor and Board Member of many journals. He is Editor of Springer Book Series in Reliability Engineering and has served as Conference Chair and Program Chair of over 40 international conferences. He is the author or coauthor of 8 books and has published over 200 journal articles, 100 conference papers and edited 17 books including Springer Handbook in Engineering Statistics and Handbook in Reliability Engineering. He has delivered over 40 invited keynote and plenary speeches at many international conferences and institutions.

His numerous awards include the 2009 IEEE Reliability Society Engineer of the Year Award. He is Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the Institute of Industrial Engineers (IIE).

Probability, Statistics, and Reliability Concepts.- Distribution Functions and Its Applications.- Statistical Parameter Estimation.- System Reliability Modeling.- Order Statistics and Reliability Estimation.- Stochastic Processes.- Maintenance Modeling.- Software Reliability.- Statistical Machine Learning Methods and Its Applications.

This book presents the state-of-the-art methodology and detailed analytical models and methods used to assess the reliability of complex systems and related applications in statistical reliability engineering. It is a textbook based mainly on the author's recent research and publications as well as experience of over 30 years in this field.

The book covers a wide range of methods and models in reliability, and their applications, including:

- statistical methods and model selection for machine learning;
- models for maintenance and software reliability;
- statistical reliability estimation of complex systems; and
- statistical reliability analysis of k out of n systems, standby systems and repairable systems.

Offering numerous examples and solved problems within each chapter, this comprehensive text provides an introduction to reliability engineering graduate students, a reference for data scientists and reliability engineers, and a thorough guide for researchers and instructors in the field.

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