There is a need to optimise the maintenance of nuclear power plants, both to improve reliability and increase competitiveness. The tendency is to move from preventative (time based) maintenance to one dependent on the condition of plant and its components. This publication collects and analyses proven condition based maintenance strategies and techniques in Member States as well as selected papers on maintenance optimisation.

This book introduces advanced methods of computational and information systems allowing readers to better understand the state-of-the-art design and implementation technology needed to maintain and enhance the safe operation of nuclear power plants. The subjects dealt with in the book are (i) Full digital instrumentation and control systems and human–machine interface technologies (ii) Risk monitoring methods for large and complex plants (iii) Condition monitors for plant components (iv) Virtual and augmented reality for nuclear power plants and (v) Software reliability verification and validation for nuclear power plants. The target readers of this book are Ph.D. students, researchers and engineers in the field of nuclear power engineering.

In a society that praises and promotes technological advancement, it becomes increasingly essential to review the effects of such rapid technological growth. New high-tech advances need to be examined to determine what they mean to science, society, and industry along with the benefits and challenges they present. The Handbook of Research on Industrial Advancement in Scientific Knowledge addresses the intersection of technology and science where engineering considerations, mathematical approaches, and management tools provide a better understanding and awareness of Industry 4.0, while also taking into account the impact on current society. This publication identifies methodologies and applications related to decision making, risk and uncertainty, and design and development not only on scientific and industrial topics but also on social and ethical matters. It is designed for engineers, entrepreneurs, academicians, researchers, managers, and students.

As Industry 4.0 brings on a new bout of transformation and fundamental changes in various industries, the traditional manufacturing and production methods are falling to the wayside. Industrial processes must embrace modern technology and the most recent trends to keep up with the times. With “smart factories”; the automation of information and data; and the inclusion of IoT, AI technologies, robotics, and cloud computing comes new challenges to tackle. These changes are creating new threats in security, reliability, the regulations around legislation and standardization of technologies, malfunctioning devices or operational disruptions, and more. These effects span a variety of industries and need to be discussed. Research Anthology on Cross-Industry Challenges of Industry 4.0 explores the challenges that have risen as multidisciplinary industries adapt to the Fourth Industrial Revolution. With a shifting change in technology, operations, management, and business models, the impacts of Industry 4.0 and digital transformation will be long-lasting and will forever change the face of manufacturing and production. This book highlights a cross-industry view of these challenges, the impacts they have, potential solutions, and the technological advances that have brought about these new issues. It is ideal for mechanical engineers, electrical engineers, manufacturers, supply chain managers, logistics specialists, investors, managers, policymakers, production scientists, researchers, academicians, and students looking for cross-industry research on the challenges associated with Industry 4.0.

Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:Addresses nearest-neighbor-based, clustering-based, statistical, and information theory-based techniquesConsiders the merits of e

This publication deals with the latest nuclear power plant maintenance optimization programmes and provides key requirements and strategies for successful implementation. It documents shared proven maintenance optimization methods and techniques from Member States, including more detailed examples in the annexes of this publication.

Please note: this publication is superseded by NS-G-2.6

This volume gathers the latest advances, innovations, and applications in the field of structural health monitoring (SHM) and more broadly in the fields of smart materials and intelligent systems. The volume covers highly diverse topics, including signal processing, smart sensors, autonomous systems, remote sensing and support, UAV platforms for SHM, Internet of Things, Industry 4.0, and SHM for civil structures and infrastructures. The contributions, which are published after a rigorous international peer-review process, highlight numerous exciting ideas that will spur novel research directions and foster multidisciplinary collaboration among different specialists. The contents of this volume reflect the outcomes of the activities of EWSHM (European Workshop on Structural Health Monitoring) in 2020.

In the fast pace of the modern world it is important, more than ever, for factories to know how and why their machines are failing and what can be done to prevent it. As such, it is imperative that new research is conducted to make sure that factories can operate as efficiently as possible. Fuzzy Logic Dynamics and Machine Prediction for Failure Analysis is an essential reference source for the newest research on the risk assessment matrix, ladder logic, and computerized maintenance management systems (CMMS). Featuring widespread coverage across a variety of related viewpoints and topics, such as the Ishikawa diagram, machinery failure analysis and troubleshooting, model reference adaptive control systems, and proportional–integral–derivative (PID) controllers, this book is ideally designed for professionals, upper-level students, and academics seeking current research on the implementation of fuzzy logic in machine prediction failure.