Predictive Maintenance In Dynamic Systems
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| Author | : Edwin Lughofer |
| Publisher | : Springer |
| Total Pages | : 564 |
| Release | : 2019-02-28 |
| Genre | : Technology & Engineering |
| ISBN | : 3030056457 |
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.
| Author | : Edwin Lughofer |
| Publisher | : Springer |
| Total Pages | : 567 |
| Release | : 2019-03-25 |
| Genre | : Technology & Engineering |
| ISBN | : 9783030056445 |
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.
| Author | : R. Keith Mobley |
| Publisher | : Elsevier |
| Total Pages | : 451 |
| Release | : 2002-10-24 |
| Genre | : Technology & Engineering |
| ISBN | : 0080478697 |
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
| Author | : Edwin Lughofer |
| Publisher | : |
| Total Pages | : |
| Release | : 2019 |
| Genre | : Electronic books |
| ISBN | : 9783030056469 |
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. .
| Author | : Rafael Gouriveau |
| Publisher | : John Wiley & Sons |
| Total Pages | : 187 |
| Release | : 2016-10-14 |
| Genre | : Technology & Engineering |
| ISBN | : 1119371023 |
This book addresses the steps needed to monitor health assessment systems and the anticipation of their failures: choice and location of sensors, data acquisition and processing, health assessment and prediction of the duration of residual useful life. The digital revolution and mechatronics foreshadowed the advent of the 4.0 industry where equipment has the ability to communicate. The ubiquity of sensors (300,000 sensors in the new generations of aircraft) produces a flood of data requiring us to give meaning to information and leads to the need for efficient processing and a relevant interpretation. The process of traceability and capitalization of data is a key element in the context of the evolution of the maintenance towards predictive strategies.
| Author | : Amit Kumar Tyagi |
| Publisher | : CRC Press |
| Total Pages | : 419 |
| Release | : 2024-10-23 |
| Genre | : Computers |
| ISBN | : 1040151396 |
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.
| Author | : Wang, John |
| Publisher | : IGI Global |
| Total Pages | : 3296 |
| Release | : 2023-01-20 |
| Genre | : Computers |
| ISBN | : 1799892212 |
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.
| Author | : Fan-Tien Cheng |
| Publisher | : John Wiley & Sons |
| Total Pages | : 562 |
| Release | : 2021-10-26 |
| Genre | : Technology & Engineering |
| ISBN | : 1119739896 |
Industry 4.1 Intelligent Manufacturing with Zero Defects Discover the future of manufacturing with this comprehensive introduction to Industry 4.0 technologies from a celebrated expert in the field Industry 4.1: Intelligent Manufacturing with Zero Defects delivers an in-depth exploration of the functions of intelligent manufacturing and its applications and implementations through the Intelligent Factory Automation (iFA) System Platform. The book’s distinguished editor offers readers a broad range of resources that educate and enlighten on topics as diverse as the Internet of Things, edge computing, cloud computing, and cyber-physical systems. You’ll learn about three different advanced prediction technologies: Automatic Virtual Metrology (AVM), Intelligent Yield Management (IYM), and Intelligent Predictive Maintenance (IPM). Different use cases in a variety of manufacturing industries are covered, including both high-tech and traditional areas. In addition to providing a broad view of intelligent manufacturing and covering fundamental technologies like sensors, communication standards, and container technologies, the book offers access to experimental data through the IEEE DataPort. Finally, it shows readers how to build an intelligent manufacturing platform called an Advanced Manufacturing Cloud of Things (AMCoT). Readers will also learn from: An introduction to the evolution of automation and development strategy of intelligent manufacturing A comprehensive discussion of foundational concepts in sensors, communication standards, and container technologies An exploration of the applications of the Internet of Things, edge computing, and cloud computing The Intelligent Factory Automation (iFA) System Platform and its applications and implementations A variety of use cases of intelligent manufacturing, from industries like flat-panel, semiconductor, solar cell, automotive, aerospace, chemical, and blow molding machine Perfect for researchers, engineers, scientists, professionals, and students who are interested in the ongoing evolution of Industry 4.0 and beyond, Industry 4.1: Intelligent Manufacturing with Zero Defects will also win a place in the library of laypersons interested in intelligent manufacturing applications and concepts. Completely unique, this book shows readers how Industry 4.0 technologies can be applied to achieve the goal of Zero Defects for all product
| Author | : Steven L. Brunton |
| Publisher | : Cambridge University Press |
| Total Pages | : 615 |
| Release | : 2022-05-05 |
| Genre | : Computers |
| ISBN | : 1009098489 |
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
| Author | : Gururaj H L |
| Publisher | : CRC Press |
| Total Pages | : 204 |
| Release | : 2024-06-28 |
| Genre | : Computers |
| ISBN | : 1040018696 |
In today’s digitally interconnected world, the threat landscape has evolved to include not just sophisticated technical exploits but also the art of human manipulation. Social engineering attacks have emerged as a formidable and often underestimated threat to information security. The primary aim of this textbook is to provide a comprehensive and in-depth exploration of social engineering attacks. The book seeks to equip cybersecurity professionals, IT practitioners, students, and anyone concerned with information security with the knowledge and tools needed to recognize, prevent, and mitigate the risks posed by social engineering. The scope of this textbook is broad and multifaceted. It covers a wide range of social engineering attack vectors, including phishing, vishing, pretexting, baiting, tailgating, impersonation, and more. Each attack vector is dissected, with detailed explanations of how they work, real-world examples, and countermeasures. Key Features • Comprehensive Coverage: Thorough exploration of various social engineering attack vectors, including phishing, vishing, pretexting, baiting, quid pro quo, tailgating, impersonation, and more. • Psychological Insights: In-depth examination of the psychological principles and cognitive biases that underlie social engineering tactics. • Real-World Case Studies: Analysis of real-world examples and high-profile social engineering incidents to illustrate concepts and techniques. • Prevention and Mitigation: Practical guidance on how to recognize, prevent, and mitigate social engineering attacks, including security best practices. • Ethical Considerations: Discussion of ethical dilemmas and legal aspects related to social engineering that emphasizes responsible use of knowledge. This comprehensive textbook on social engineering attacks provides a deep and practical exploration of this increasingly prevalent threat in cybersecurity. It covers a wide array of attack vectors, including phishing, vishing, pretexting, and more, offering readers an in-depth understanding of how these attacks work. The book delves into the psychology behind social engineering and examines the cognitive biases and emotional triggers that make individuals susceptible. Real-world case studies illustrate concepts and techniques while practical guidance equips readers with the knowledge to recognize, prevent, and mitigate social engineering threats.
