The emphasis of this textbook is on industrial applications of Statistical Measurement Theory. It deals with the principal issues of measurement theory, is concise and intelligibly written, and to a wide extent self-contained. Difficult theoretical issues are separated from the mainstream presentation. Each topic starts with an informal introduction followed by an example, the rigorous problem formulation, solution method, and a detailed numerical solution. Each chapter concludes with a set of exercises of increasing difficulty, mostly with solutions. The book is meant as a text for graduate students and a reference for researchers and industrial experts specializing in measurement and measurement data analysis for quality control, quality engineering and industrial process improvement using statistical methods. Knowledge of calculus and fundamental probability and statistics is required for the understanding of its contents.

This textbook on Reliability Theory focusses on Applications in Preventive Maintenance (PM). All models are presented in connection with the relevant statistical material. Short and simply written the book is almost self-contained. The reader needs not more than basic knowledge of calculus, probability and statistics. Each chapter is concluded by a series of exercices with detailed solutions. Numerical solutions are elaborated with Mathematica software. Novel topics are discussed, like PM with learning, choice of the best time-scale for PM, handling multidimensional state description, dealing with uncertainty in data. The book is meant for graduate students, researchers and engineers specializing in Quality Control, Logistics, Reliability and Maintenance Engineering.

Ternary means "based on three". This book deals with reliability investigations of networks whose components subject to failures can be in three states -up, down and middle (mid), contrary to traditionally considered networks having only binary (up/down) components. Extending binary case to ternary allows to consider more realistic and flexible models for communication, flow and supply networks

The material in this book was first presented as a one-semester course in Relia bility Theory and Preventive Maintenance for M.Sc. students of the Industrial Engineering Department of Ben Gurion University in the 1997/98 and 1998/99 academic years. Engineering students are mainly interested in the applied part of this theory. The value of preventive maintenance theory lies in the possibility of its imple mentation, which crucially depends on how we handle statistical reliability data. The very nature of the object of reliability theory - system lifetime - makes it extremely difficult to collect large amounts of data. The data available are usu ally incomplete, e.g. heavily censored. Thus, the desire to make the course material more applicable led me to include in the course topics such as mod eling system lifetime distributions (Chaps. 1,2) and the maximum likelihood techniques for lifetime data processing (Chap. 3). A course in the theory of statistics is aprerequisite for these lectures. Stan dard courses usually pay very little attention to the techniques needed for our purpose. A short summary of them is given in Chap. 3, including widely used probability plotting. Chapter 4 describes the most useful and popular models of preventive main tenance and replacement. Some practical aspects of applying these models are addressed, such as treating uncertainty in the data, the role of data contamina tion and the opportunistic scheduling of maintenance activities."

Well written textbook on industrial applications of Statistical Measurement Theory. It deals with the principal issues of measurement theory, is concise and intelligibly written, and to a wide extent self-contained. Difficult theoretical issues are separated from the mainstream presentation. Each topic starts with an informal introduction followed by an example, the rigorous problem formulation, solution method, and a detailed numerical solution. Chapter are concluded with a set of exercises of increasing difficulty, mostly with solutions. Knowledge of calculus and fundamental probability and statistics is assumed.

The increase in the requirements on the reliability of units makes it necessary to analyze the relationship between mathematicalmeth ods of calculating reliability and the physical nature of fail ures. The difficulty of such an analysis is obvious. On the one hand, in making a representation of the physical picture of a phe nomenon, one can make an error in the direction of excessive sim plification. On the other hand, in the mathematical treatment of the physical scheme, it may be necessary to use extremely complex and fine analytical methods, and their simplified exposition bor ders on vulgarization. Without the aid of a large number of specialists working in the field of analysis and calculation of systems reliability, an ex posi tion of models of failures and their mathematical treatment would be unobtainable. The authors take this opportunity to express their gratitude to Academicians N. G. B r u y vic e h and Y u. V. Lin n i k conversations with whom clarified a number of problems treated, to active member of the Academy of Sciences of the UkrSSR B. V."

This introductory book equips the reader to apply the core concepts and methods of network reliability analysis to real-life problems. It explains the modeling and critical analysis of systems and probabilistic networks, and requires only a minimal background in probability theory and computer programming. Based on the lecture notes of eight courses taught by the authors, the book is also self-contained, with no theory needed beyond the lectures. The primary focus is on essential "modus operandi," which are illustrated in numerous examples and presented separately from the more difficult theoretical material.

This book is devoted to the probabilistic description of the behavior of a network in the process of random removal of its components (links, nodes) appearing as a result of technical failures, natural disasters or intentional attacks. It is focused on a practical approach to network reliability and resilience evaluation, based on applications of Monte Carlo methodology to numerical approximation of network combinatorial invariants, including so-called multidimensional destruction spectra. This allows to develop a probabilistic follow-up analysis of the network in the process of its gradual destruction, to identify most important network components and to develop efficient heuristic algorithms for network optimal design. Our methodology works with satisfactory accuracy and efficiency for most applications of reliability theory to real -life problems in networks.