Practical Approaches to Reliability Theory in Cutting-Edge Applications
Probabilistic Reliability Models helps readers understand and properly use statistical methods
and optimal resource allocation to solve engineering problems.
The author supplies engineers with a deeper understanding of mathematical models while also
equipping mathematically oriented readers with a fundamental knowledge of the engineeringrelated
applications at the center of model building. The book showcases the use of probability
theory and mathematical statistics to solve common, real-world reliability problems. Following
an introduction to the topic, subsequent chapters explore key systems and models including:
- Unrecoverable objects and recoverable systems
- Methods of direct enumeration
- Markov models and heuristic models
- Performance effectiveness
- Time redundancy
- System survivability
- Aging units and their related systems
- Multistate systems
Detailed case studies illustrate the relevance of the discussed methods to real-world technical
projects including software failure avalanches, gas pipelines with underground storage, and
intercontinental ballistic missile (ICBM) control systems. Numerical examples and detailed
explanations accompany each topic, and exercises throughout allow readers to test their
comprehension of the presented material.
Probabilistic Reliability Models is an excellent book for statistics, engineering, and operations
research courses on applied probability at the upper-undergraduate and graduate levels. The
book is also a valuable reference for professionals and researchers working in industry who
would like a mathematical review of reliability models and the relevant applications.
