Reliability Based Modeling Of System Performance

Probabilistic modeling of system performance, built primarily on the foundation of reliability theory, is referred to as reliability-based modeling. This book progressively builds up the functional complexity of system modeling. It starts by modeling the simplest system that can be designed, and then moves to the functional modeling of the multi-state system, step-by-step. Reliability-based Modeling of System Performance deals with modeling the functioning of systems by presenting its concepts, methods and mathematical tools. It is accessible to engineering students and academics who have already acquired basic knowledge of probability theory, applied statistics, differential calculus, integrals, combinatorial calculus and Boolean algebra.

Probabilistic modeling of system performance, built primarily on the foundation of reliability theory, is referred to as reliability-based modeling. This book progressively builds up the functional complexity of system modeling. It starts by modeling the simplest system that can be designed, and then moves to the functional modeling of the multi-state system, step-by-step. Reliability-based Modeling of System Performance deals with modeling the functioning of systems by presenting its concepts, methods and mathematical tools. It is accessible to engineering students and academics who have already acquired basic knowledge of probability theory, applied statistics, differential calculus, integrals, combinatorial calculus and Boolean algebra.

Reliability is one of the fundamental criteria in engineering systems. Design and maintenance serve to support it throughout the systems life. As such, maintenance acts in parallel to production and can have a great impact on the availability and capacity of production and the quality of the products. The authors describe current and innovative methods useful to industry and society.

Performance and Reliability Analysis of Computer Systems: An Example-Based Approach Using the SHARPE Software Package provides a variety of probabilistic, discrete-state models used to assess the reliability and performance of computer and communication systems. The models included are combinatorial reliability models (reliability block diagrams, fault trees and reliability graphs), directed, acyclic task precedence graphs, Markov and semi-Markov models (including Markov reward models), product-form queueing networks and generalized stochastic Petri nets. A practical approach to system modeling is followed; all of the examples described are solved and analyzed using the SHARPE tool. In structuring the book, the authors have been careful to provide the reader with a methodological approach to analytical modeling techniques. These techniques are not seen as alternatives but rather as an integral part of a single process of assessment which, by hierarchically combining results from different kinds of models, makes it possible to use state-space methods for those parts of a system that require them and non-state-space methods for the more well-behaved parts of the system. The SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) package is the `toolchest' that allows the authors to specify stochastic models easily and solve them quickly, adopting model hierarchies and very efficient solution techniques. All the models described in the book are specified and solved using the SHARPE language; its syntax is described and the source code of almost all the examples discussed is provided. Audience: Suitable for use in advanced level courses covering reliability and performance of computer and communications systems and by researchers and practicing engineers whose work involves modeling of system performance and reliability.

Recent developments in reliability engineering has become the most challenging and demanding area of research. Modeling and Simulation, along with System Reliability Engineering has become a greater issue because of high-tech industrial processes, using more complex systems today. This book gives the latest research advances in the field of modeling and simulation, based on analysis in engineering sciences. Features Focuses on the latest research in modeling and simulation based analysis in reliability engineering. Covers performance evaluation of complex engineering systems Identifies and fills the gaps of knowledge pertaining to engineering applications Provides insights on an international and transnational scale Modeling and Simulation Based Analysis in Reliability Engineering aims at providing a reference for applications of mathematics in engineering, offering a theoretical sound background with adequate case studies, and will be of interest to researchers, practitioners, and academics.

This book describes methods to improve software performance and safety using advanced mathematical and computational analytics. The main focus is laid on the increase of software reliability by preventive and predictive maintenance with efficient usage of modern testing resources. The editors collect contributions from international researchers in the field.

Reliability is one of the fundamental criteria in engineering systems. Design and maintenance serve to support it throughout the systems life. As such, maintenance acts in parallel to production and can have a great impact on the availability and capacity of production and the quality of the products. The authors describe current and innovative methods useful to industry and society.

This book is intended to provide the interested reader with basic information on various issues of the dependability analysis and evaluation of engineering systems with the principal goal to help the reader perform such an analysis and evaluation. By the definition of the IEC International Standard 50(191) dependability is the collective term used to describe the availability peiformance and its influencing factors: reliability peiformance, maintainability peiformance and maintenance support performance. Dependability is a term used for a general description of system performance but not a quality which could be expressed by a single quantitative measure. There are several other quantitative terms, such as reliability, unreliability, time-specific and steady-state availability and unavailability, which together form a basis for evaluating the dependability of a system. A system is taken as dependable if it satisfies all requirements of the customers with regard to various dependability performances and indices. The dependability deals with failures, repairs, preventive maintenance as well as with costs associated with investment and service interruptions or mission failures. Therefore, it is a very important attribute of system quality. The dependability evaluation is strongly based upon experience and statistical data on the behavior of a system and of its elements. Using past experience with the same or similar systems and elements, the prospective operation may be predicted and improved designs and constructions can be conceived. Hence, the dependability analysis makes it possible to learn from the past for better future solutions.