Reliability Assessment and Optimization of Complex Systems delves into a range of tools and techniques for designing optimized complex systems. Each chapter explores system modeling and the implementation of various metaheuristics for optimization purposes. This book provides readers in the domain of applied mathematics with a comprehensive understanding of system reliability analysis and improvement, thereby offering substantial value to their knowledge and expertise. System reliability has become the paramount attribute of any production unit. The process of maximizing system reliability while adhering to multiple constraints is referred to as reliability optimization.
There are two primary approaches to enhancing a system's performance and reliability: developing a product with reduced failures (failure avoidance) or incorporating resilience to ensure the system continues functioning even in the event of a failure (fault tolerance).
1. 1 Introduction - p. 309 2. 2 Assumptions - p. 312 3. 3 Example of a real industry model - p. 313 4. 4 Notations and state of the system - p. 314 5. 5 Transition probabilities and mean sojourn time - p. 314 6. 5.6 Busy period analysis - p. 325 7. 5.7 Cost-benefit analysis - p. 328 8. 6 Estimation of the parameters, MTSF and profit function - p. 328 9. 6.1 Likelihood function - p. 328 10. 6.2 Bayesian estimation - p. 330 11. 6.3 Simulation study - p. 331 12. 7 Conclusion - p. 340 13. References - p. 340 14. Chapter 17 Optimal design of a reinforced concrete beam considering uncertainties - p. 343 15. Chapter 18 Reliability-based design optimization for cyber survivability - p. 363 16. Chapter 19 Determining optimum inspection interval of critical railway rolling stock components using discrete chain Markov modeling - p. 413 17. Chapter 20 Analysis of sensor reliability in IoT solutions - p. 435 18. Index - p. 455
Reliability Assessment and Optimization of Complex Systems delves into a range of tools and techniques for designing optimized complex systems. Each chapter explores system modeling and the implementation of various metaheuristics for optimization purposes. This book provides readers in the domain of applied mathematics with a comprehensive understanding of system reliability analysis and improvement, thereby offering substantial value to their knowledge and expertise. System reliability has become the paramount attribute of any production unit. The process of maximizing system reliability while adhering to multiple constraints is referred to as reliability optimization.
There are two primary approaches to enhancing a system's performance and reliability: developing a product with reduced failures (failure avoidance) or incorporating resilience to ensure the system continues functioning even in the event of a failure (fault tolerance).
1. 1 Introduction - p. 309 2. 2 Assumptions - p. 312 3. 3 Example of a real industry model - p. 313 4. 4 Notations and state of the system - p. 314 5. 5 Transition probabilities and mean sojourn time - p. 314 6. 5.6 Busy period analysis - p. 325 7. 5.7 Cost-benefit analysis - p. 328 8. 6 Estimation of the parameters, MTSF and profit function - p. 328 9. 6.1 Likelihood function - p. 328 10. 6.2 Bayesian estimation - p. 330 11. 6.3 Simulation study - p. 331 12. 7 Conclusion - p. 340 13. References - p. 340 14. Chapter 17 Optimal design of a reinforced concrete beam considering uncertainties - p. 343 15. Chapter 18 Reliability-based design optimization for cyber survivability - p. 363 16. Chapter 19 Determining optimum inspection interval of critical railway rolling stock components using discrete chain Markov modeling - p. 413 17. Chapter 20 Analysis of sensor reliability in IoT solutions - p. 435 18. Index - p. 455
