Statistical Reliability Engineering [electronic resource] : Methods, Models and Applications / by Hoang Pham.
Material type: Computer filePublication details: Cham : Springer International Publishing : Imprint: Springer, 2022.Edition: 1st ed. 2022Description: XX, 497 p. 54 illus. online resourceISBN:- 9783030769048
- Industrial Management
- Security systems
- StatisticsÂ
- Industrial engineering
- Production engineering
- Computers
- Computer science-Mathematics
- Mathematical statistics
- Industrial Management
- Security Science and Technology
- Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
- Industrial and Production Engineering
- Hardware Performance and Reliability
- Probability and Statistics in Computer Science
- 658.5Â 23
Item type | Home library | Call number | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|
e-Book | S. R. Ranganathan Learning Hub Online | 658.5 (Browse shelf(Opens below)) | Available | EB1474 |
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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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