Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers [electronic resource] /
Lisnianski, Anatoly.author.
Frenkel, Ilia.author.
Ding, Yi.author.
Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers presents a comprehensive, up-to-date description of multi-state system (MSS) reliability as a natural extension of classical binary-state reliability. It presents all essential theoretical achievements in the field, but is also practically oriented. New theoretical issues are described, including: combined Markov and semi-Markov processes methods, and universal generating function techniques; statistical data processing for MSSs; reliability analysis of aging MSSs; methods for cost-reliability and cost-availability analysis of MSSs; and main definitions and concepts of fuzzy MSS. Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers also discusses life cycle cost analysis and practical optimal decision making for real world MSSs. Numerous examples are included in each section in order to illustrate mathematical tools. Besides these examples, real world MSSs (such as power generating and transmission systems, air-conditioning systems, production systems, etc.) are considered as case studies. Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers also describes basic concepts of MSS, MSS reliability measures and tools for MSS reliability assessment and optimization. It is a self-contained study resource and does not require prior knowledge from its readers, making the book attractive for researchers as well as for practical engineers and industrial managers.
Description based upon print version of record.
Includes bibliographical references and index.
Multi-state Systems in Nature and in Engineering -- Modern Stochastic Process Methods for Multi-state System Reliability Assessment -- Statistical Analysis of Reliability Data for Multi-state Systems -- Universal Generating Function Method -- Combined Universal Generating Function and Stochastic Process Method -- Reliability-associated Cost Assessment and Management Decisions for Multi-state Systems -- Aging Multi-state Systems -- Fuzzy Multi-state System: General Definition and Reliability Assessment.
Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers presents a comprehensive, up-to-date description of multi-state system (MSS) reliability as a natural extension of classical binary-state reliability. It presents all essential theoretical achievements in the field, but is also practically oriented. New theoretical issues are described, including: combined Markov and semi-Markov processes methods, and universal generating function techniques; statistical data processing for MSSs; reliability analysis of aging MSSs; methods for cost-reliability and cost-availability analysis of MSSs; and main definitions and concepts of fuzzy MSS. Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers also discusses life cycle cost analysis and practical optimal decision making for real world MSSs. Numerous examples are included in each section in order to illustrate mathematical tools. Besides these examples, real world MSSs (such as power generating and transmission systems, air-conditioning systems, production systems, etc.) are considered as case studies. Multi-state System Reliability Analysis and Optimization for Engineers and Industrial Managers also describes basic concepts of MSS, MSS reliability measures and tools for MSS reliability assessment and optimization. It is a self-contained study resource and does not require prior knowledge from its readers, making the book attractive for researchers as well as for practical engineers and industrial managers.
Quality control.
Reliability.
Industrial safety.
Production management.
Probabilities.
Computational intelligence.
Engineering economics.
Engineering economy.
Quality Control, Reliability, Safety and Risk.
Operations Management.
Probability Theory and Stochastic Processes.
Computational Intelligence.
Engineering Economics, Organization, Logistics, Marketing.