Bøger af Hoang Pham

This book provides engineers and researchers knowledge to help them in system reliability analysis using machine learning, artificial intelligence, big data, genetic algorithm, information theory, multi-criteria decision making, and other techniques. It will also be useful to students learning reliability engineering.The book brings readers up to date with how system reliability relates to the latest techniques of AI, big data, genetic algorithm, information theory, and multi-criteria decision making and points toward future developments in the subject.

This book covers advanced reliability and maintainability knowledge as applied to recent engineering problems. It highlights research in the fields of reliability measures of binary and complex engineering systems, cost analysis, simulations, optimizations, risk factors, and sensitivity analysis. The book scrutinizes various advanced tools and techniques, methodology, and concepts to solve the various engineering problems related to reliability and maintainability of the industrial system at minimum cost and maximum profit. It consists of 15 chapters and offers a platform to researchers, academicians, professionals and scientists to enhance their knowledge and understanding the concept of reliability in engineering.

Engineers and practitioners contribute to society through their ability to apply basic scientific principles to real problems in an effective and efficient manner. They must collect data to test their products every day as part of the design and testing process and also after the product or process has been rolled out to monitor its effectiveness. Model building and validation, data collection, data analysis and data interpretation form the core of sound engineering practice.After the data has been gathered the engineers, statisticians, designers, and practitioners must be able to sift them and interpret them correctly so that meaning can be exposed from a mass of undifferentiated numbers or facts. To do this he must be familiar with the fundamental concepts of correlation, uncertainty, variability and risk in the face of uncertainty.In today's global and highly competitive environment, continuous improvement in the processes and products of any field of engineeringis essential for survival. Many organizations have shown that the first step to continuous improvement is to integrate the widespread use of statistics and basic data analysis into the manufacturing development process as well as into the day-to-day business decisions taken in regard to engineering and technological information processes.The Springer Handbook of Engineering Statistics gathers together the full range of statistical techniques required by readers from all fields to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved. Key TopicsFundamental Statistics Process Monitoring and ImprovementReliability Modeling and Survival AnalysisRegression MethodsData MiningStatistical Methods and ModelingWide Range of Applications including Six Sigma FeaturesContributions from leading experts in statistics and their application to engineering from industrial control to academic medicine and financial risk management Wide-ranging selection of statistical techniques to enable the readers to choose the method most appropriate Extensive and easy-to-use subject index making information quickly available to the reader. The Springer Handbook of Engineering Statistics will be essential reading for all engineers, statisticians, researchers, teachers, students, and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.

This book presents the state-of-the-art methodology and detailed analytical models and methods used to assess the reliability of complex systems and related applications in statistical reliability engineering.

Presents the theories and methods of reliability and maintenance, emphasising multi-component systems. Aimed at postgraduates, researchers and practitioners, this title covers: imperfect repair, economic dependence and opportunistic maintenance, failure and repair, software reliability and maintenance cost, and warranty cost considerations.

This book is an introduction to software reliability engineering and a survey of the state-of-the-art techniques, methodologies and tools used to assess the reliability of software and combined software-hardware systems.

Companies are being forced to process and manage increasingly differentiated products with shorter life cycles, low volumes and reduced customer delivery times. This book introduces an original and integrated idea of maintenance: maintenance for productivity.

Software Reliability Assessment with OR Applications provides a comprehensive guide to software reliability measurement, prediction, and control. Readers will find solutions to decision-making problems that software developers and engineers often face.

This book presents 14 recent papers that provide an overview of the latest software reliability models, testing techniques, and applications. The book is targeted toward design engineers, software engineers, researchers, computer scientists, technical managers, and students wishing to conduct research or update their knowledge in the field of software reliability and testing. The papers on software reliability describe applications of software reliability growth modeling to software systems, investigate the relationship between program complexity measures and program errors, and explore the difficulty in accurately modeling software reliability. The remaining papers present new models that are useful in estimating the reliability of software systems and deal with the cost-reliability-optimal software release policies in software systems. The papers on software testing investigate the relationship between reliability and testing, discuss safety arguments and the role of software and system reliability and describe the use of an operational profile in testing a large-scale industrial software system for estimating software. The remaining papers present new models useful in testing software systems.