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Discover the future of industrial maintenance with "IoT Maintenance: Predictive Techniques for Smart Equipment," a cutting-edge guide to transforming your maintenance strategies through the power of the Internet of Things (IoT). This book is an essential resource for professionals seeking to leverage IoT technology to enhance operational efficiency and reduce downtime. With a focus on predictive maintenance, this guide is tailored to revolutionize your approach to equipment management and maintenance across various industries. What You Will Find in This Book: - Insights into IoT Systems: Learn how IoT devices and sensors work synergistically to monitor equipment health and predict maintenance needs. - Predictive Maintenance Strategies: Detailed discussions on how to implement IoT-based predictive maintenance to anticipate and prevent potential failures. - Industry-Specific Applications: Explore tailor-made IoT solutions across diverse sectors including manufacturing, healthcare, agriculture, and energy. - Practical Case Studies: Real-world success stories that illustrate the profound impact of IoT on enhancing operational resilience and safety. - Technological Foundations: Understand the core technologies and infrastructure that support IoT operations, from cloud computing to data analytics. - Security and Data Management: Essential guidance on navigating the complexities of data security and privacy in IoT deployments. - Future Trends and Innovations: Look ahead at emerging IoT technologies and trends that are set to redefine maintenance practices. Embrace the technological revolution in industrial maintenance with "IoT Maintenance: Predictive Techniques for Smart Equipment" and set the stage for a more efficient, reliable, and sustainable future.
This book presents the outcome of the European project "SERENA", involving fourteen partners as international academics, technological companies, and industrial factories, addressing the design and development of a plug-n-play end-to-end cloud architecture, and enabling predictive maintenance of industrial equipment to be easily exploitable by small and medium manufacturing companies with a very limited data analytics experience. Perspectives and new opportunities to address open issues on predictive maintenance conclude the book with some interesting suggestions of future research directions to continue the growth of the manufacturing intelligence.
This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.
This book provides a comprehensive guide to Industry 4.0 applications, not only introducing implementation aspects but also proposing a conceptual framework with respect to the design principles. In addition, it discusses the effects of Industry 4.0, which are reflected in new business models and workforce transformation. The book then examines the key technological advances that form the pillars of Industry 4.0 and explores their potential technical and economic benefits using examples of real-world applications. The changing dynamics of global production, such as more complex and automated processes, high-level competitiveness and emerging technologies, have paved the way for a new generation of goods, products and services. Moreover, manufacturers are increasingly realizing the value of the data that their processes and products generate. Such trends are transforming manufacturing industry to the next generation, namely Industry 4.0, which is based on the integration of information and communication technologies and industrial technology.The book provides a conceptual framework and roadmap for decision-makers for this transformation
This second edition of An Introduction to Predictive Maintenance helps plant, process, maintenance and reliability managers and engineers to develop and implement a comprehensive maintenance management program, providing proven strategies for regularly monitoring critical process equipment and systems, predicting machine failures, and scheduling maintenance accordingly. Since the publication of the first edition in 1990, there have been many changes in both technology and methodology, including financial implications, the role of a maintenance organization, predictive maintenance techniques, various analyses, and maintenance of the program itself. This revision includes a complete update of the applicable chapters from the first edition as well as six additional chapters outlining the most recent information available. Having already been implemented and maintained successfully in hundreds of manufacturing and process plants worldwide, the practices detailed in this second edition of An Introduction to Predictive Maintenance will save plants and corporations, as well as U.S. industry as a whole, billions of dollars by minimizing unexpected equipment failures and its resultant high maintenance cost while increasing productivity. - A comprehensive introduction to a system of monitoring critical industrial equipment - Optimize the availability of process machinery and greatly reduce the cost of maintenance - Provides the means to improve product quality, productivity and profitability of manufacturing and production plants
ICCECE is an international conference hosted by Techno India University, Kolkata covering research aspects in Computer, Electrical and Communication Engineering The conference invites International and SAARC Participants to present research papers
Today, in this smart era, data analytics and artificial intelligence (AI) play an important role in predictive maintenance (PdM) within the manufacturing industry. This innovative approach aims to optimize maintenance strategies by predicting when equipment or machinery is likely to fail so that maintenance can be performed just in time to prevent costly breakdowns. This book contains up-to-date information on predictive maintenance and the latest advancements, trends, and tools required to reduce costs and save time for manufacturers and industries. Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing provides an extensive and in-depth exploration of the intersection of data analytics, artificial intelligence, and predictive maintenance in the manufacturing industry and covers fundamental concepts, advanced techniques, case studies, and practical applications. Using a multidisciplinary approach, this book recognizes that predictive maintenance in manufacturing requires collaboration among engineers, data scientists, and business professionals and includes case studies from various manufacturing sectors showcasing successful applications of predictive maintenance. The real-world examples explain the useful benefits and ROI achieved by organizations. The emphasis is on scalability, making it suitable for both small and large manufacturing operations, and readers will learn how to adapt predictive maintenance strategies to different scales and industries. This book presents resources and references to keep readers updated on the latest advancements, tools, and trends, ensuring continuous learning. Serving as a reference guide, this book focuses on the latest advancements, trends, and tools relevant to predictive maintenance and can also serve as an educational resource for students studying manufacturing, data science, or related fields.
A ready reference for the next-generation discovery techniques, this book presents advanced research findings on resource discovery, network navigability, and trust management on the Internet of Things (IoT) and Social Internet of Things (SIoT) ecosystems. It discusses the benefits of integrating social networking concepts into the Internet of Things to find the preferable, reliable, scalable, and near-optimal detection of things or services. It explores the concepts of the Social Internet of Things in different domains of IoT, such as the Internet of Vehicles and the Industrial Internet of Things. This book works to recognize and respond to user queries and improve service provisioning, find the optimal solution for the link selection in the SIoT structure, develop large-scale platforms, and provide a smart mechanism for trust evaluation. · Covers the rapid advancements in low-cost sensor manufacturing, communication protocols, embedded systems, actuators, and hardware miniaturization that have contributed to the exponential growth of the IoT · Presents the fundamentals of a search system for sensor search and resource discovery in an IoT ecosystem · Includes the applicability of different search techniques across several application domains of the IoT under various use case scenarios · Discusses the thrust areas in SIoT (service discovery and composition, network navigability, relationship management, and trustworthiness management) and presents several prerequisites, challenges, and use case scenarios · Provides insights into current challenges in the domains of Internet of Things and Social Internet of Things This book will be helpful to researchers, scholars, and postgraduate students in Computer Science and Information Technology departments.
Non-woven Fabrics is differentiated text which covers overall stream from raw fibers to final products and includes features of manufacturing and finish process with specialized application end use. Application range of non-woven fabrics is extended to all the industrial fields needless to say apparel, such as ICT (information and communication technology), bio- and medicals, automobiles, architectures, construction and environmental. Every chapter is related to the important and convergent fields with the technical application purpose from downstream to upstream fields. Also, applicability of non-woven fabrics is introduced to be based on the structural analysis of dimensional concept and various non-woven fabrics as a state-of-art embedded convergent material are emphasized in all industry fields by using nanofibers and carbon fibers.