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This book introduces the methods for predicting the future behavior of a system’s health and the remaining useful life to determine an appropriate maintenance schedule. The authors introduce the history, industrial applications, algorithms, and benefits and challenges of PHM (Prognostics and Health Management) to help readers understand this highly interdisciplinary engineering approach that incorporates sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering. It is ideal for beginners because it introduces various prognostics algorithms and explains their attributes, pros and cons in terms of model definition, model parameter estimation, and ability to handle noise and bias in data, allowing readers to select the appropriate methods for their fields of application.Among the many topics discussed in-depth are:• Prognostics tutorials using least-squares• Bayesian inference and parameter estimation• Physics-based prognostics algorithms including nonlinear least squares, Bayesian method, and particle filter• Data-driven prognostics algorithms including Gaussian process regression and neural network• Comparison of different prognostics algorithms divThe authors also present several applications of prognostics in practical engineering systems, including wear in a revolute joint, fatigue crack growth in a panel, prognostics using accelerated life test data, fatigue damage in bearings, and more. Prognostics tutorials with a Matlab code using simple examples are provided, along with a companion website that presents Matlab programs for different algorithms as well as measurement data. Each chapter contains a comprehensive set of exercise problems, some of which require Matlab programs, making this an ideal book for graduate students in mechanical, civil, aerospace, electrical, and industrial engineering and engineering mechanics, as well as researchers and maintenance engineers in the above fields.
An indispensable guide for engineers and data scientists in design, testing, operation, manufacturing, and maintenance A road map to the current challenges and available opportunities for the research and development of Prognostics and Health Management (PHM), this important work covers all areas of electronics and explains how to: assess methods for damage estimation of components and systems due to field loading conditions assess the cost and benefits of prognostic implementations develop novel methods for in situ monitoring of products and systems in actual life-cycle conditions enable condition-based (predictive) maintenance increase system availability through an extension of maintenance cycles and/or timely repair actions; obtain knowledge of load history for future design, qualification, and root cause analysis reduce the occurrence of no fault found (NFF) subtract life-cycle costs of equipment from reduction in inspection costs, downtime, and inventory Prognostics and Health Management of Electronics also explains how to understand statistical techniques and machine learning methods used for diagnostics and prognostics. Using this valuable resource, electrical engineers, data scientists, and design engineers will be able to fully grasp the synergy between IoT, machine learning, and risk assessment.
PHM Society established International Journal of Prognostics and Health Management (IJPHM) in 2009 to facilitate archival publication of peer-reviewed results from research and development in the area of PHM. As a journal solely dedicated to the emerging field of PHM IJPHM is the first of its kind and has been a focal point for dissemination of peer-reviewed PHM knowledge. While for the first few years the journal maintained only an online presence, the printed volumes will now be available and can be obtained upon request.
This book is based on the accepted research papers presented in the International Conference "Artificial Intelligence in Engineering & Science" (AIES-2022). The aim of the AIES Conference is to bring together researchers involved in the theory of computational intelligence, knowledge engineering, fuzzy systems, soft computing, machine learning and related areas and applications in engineering, bioinformatics, industry, medicine, energy, smart city, social spheres and other areas. This book presents new perspective research results: models, methods, algorithms and applications in the field of Artificial Intelligence (AI). Particular emphasis is given to the medical applications - medical images recognition, development of the expert systems which could be interesting for the AI researchers as well for the physicians looking for the new ideas in medicine. The central audience of the book are researchers, industrial practitioners, students specialized in the Artificial Intelligence.
Reliability Analysis and Asset Management of Engineering Systems explains methods that can be used to evaluate reliability and availability of complex systems, including simulation-based methods. The increasing digitization of mechanical processes driven by Industry 4.0 increases the interaction between machines and monitoring and control systems, leading to increases in system complexity. For those systems the reliability and availability analyses are increasingly challenging, as the interaction between machines has become more complex, and the analysis of the flexibility of the production systems to respond to machinery failure may require advanced simulation techniques. This book fills a gap on how to deal with such complex systems by linking the concepts of systems reliability and asset management, and then making these solutions more accessible to industry by explaining the availability analysis of complex systems based on simulation methods that emphasise Petri nets. - Explains how to use a monitoring database to perform important tasks including an update of complex systems reliability - Shows how to diagnose probable machinery-based causes of system performance degradation by using a monitoring database and reliability estimates in an integrated way - Describes practical techniques for the application of AI and machine learning methods to fault detection and diagnosis problems
Zusammenfassung: This book gathers outstanding papers presented at the 18th Annual Conference of China Electrotechnical Society, organized by China Electrotechnical Society (CES), held in Nanchang, China, from September 15 to 17, 2023. It covers topics such as electrical technology, power systems, electromagnetic emission technology, and electrical equipment. It introduces the innovative solutions that combine ideas from multiple disciplines. The book is very much helpful and useful for the researchers, engineers, practitioners, research students, and interested readers
ISHM is an innovative combination of technologies and methods that offers solutions to the reliability problems caused by increased complexities in design, manufacture, use conditions, and maintenance. Its key strength is in the successful integration of reliability (quantitative estimation of successful operation or failure), "diagnosibility" (ability to determine the fault source), and maintainability (how to maintain the performance of a system in operation). It draws on engineering issues such as advanced sensor monitoring, redundancy management, probabilistic reliability theory, artificial intelligence for diagnostics and prognostics, and formal validation methods, but also "quasi-technical" techniques and disciplines such as quality assurance, systems architecture and engineering, knowledge capture, information fusion, testability and maintainability, and human factors. This groundbreaking book defines and explains this new discipline, providing frameworks and methodologies for implementation and further research. Each chapter includes experiments, numerical examples, simulations and case studies. It is the ideal guide to this crucial topic for professionals or researchers in aerospace systems, systems engineering, production engineering, and reliability engineering. - Solves prognostic information selection and decision-level information fusion issues - Presents integrated evaluation methodologies for complex aerospace system health conditions and software system reliability assessment - Proposes a framework to perform fault diagnostics with a distributed intelligent agent system and a data mining approach for multistate systems - Explains prognostic methods that combine both the qualitative system running state prognostics and the quantitative remaining useful life prediction