A System Approach To Implementation Of Predictive Maintenance With Machine Learning
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| Author | : Lorna Uden |
| Publisher | : Springer |
| Total Pages | : 732 |
| Release | : 2018-07-30 |
| Genre | : Computers |
| ISBN | : 3319952048 |
This book contains the refereed proceedings of the 13th International Conference on Knowledge Management in Organizations, KMO 2018, held in Žilina, Slovakia, in August 2018. The theme of the conference was "Emerging Research for Knowledge Management in Organizations." The 59 papers accepted for KMO 2018 were selected from 141 submissions and are organized in topical sections on: Knowledge management models and analysis; knowledge sharing; knowledge transfer and learning; knowledge and service innovation; knowledge creation; knowledge and organization; information systems and information science; knowledge and technology management; data mining and intelligent science; business and customer relationship management; big data and IoT; and new trends in IT.
| Author | : Fabienne-Fariba Salimi |
| Publisher | : Elsevier |
| Total Pages | : 443 |
| Release | : 2017-11-28 |
| Genre | : Technology & Engineering |
| ISBN | : 0128042184 |
A Systems Approach to Managing the Complexities of Process Industries discusses the principles of system engineering, system thinking, complexity thinking and how these apply to the process industry, including benefits and implementation in process safety management systems. The book focuses on the ways system engineering skills, PLM, and IIoT can radically improve effectiveness of implementation of the process safety management system. Covering lifecycle, megaproject system engineering, and project management issues, this book reviews available tools and software and presents the practical web-based approach of Analysis & Dynamic Evaluation of Project Processes (ADEPP) for system engineering of the process manufacturing development and operation phases. Key solutions proposed include adding complexity management steps in the risk assessment framework of ISO 31000 and utilization of Installation Lifecycle Management. This study of this end-to-end process will help users improve operational excellence and navigate the complexities of managing a chemical or processing plant. - Presents a review of Operational Excellence and Process Safety Management Methods, along with solutions to complexity assessment and management - Provides a comparison of the process manufacturing industry with discrete manufacturing, identifying similarities and areas of customization for process manufacturing - Discusses key solutions for managing the complexities of process manufacturing development and operational phases
| 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 | : 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 | : Joao Gama |
| Publisher | : Springer Nature |
| Total Pages | : 317 |
| Release | : 2021-01-09 |
| Genre | : Computers |
| ISBN | : 3030667707 |
This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.
| 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 | : John D. Kelleher |
| Publisher | : MIT Press |
| Total Pages | : 853 |
| Release | : 2020-10-20 |
| Genre | : Computers |
| ISBN | : 0262361108 |
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
| Author | : Abid Ali Khan |
| Publisher | : Springer Nature |
| Total Pages | : 396 |
| Release | : 2024-01-26 |
| Genre | : Technology & Engineering |
| ISBN | : 9819977754 |
This volume contains forty-one revised and extended research articles, written by prominent researchers participating in the International Conference on Aeronautical Sciences, Engineering and Technology 2023, held in Muscat, October 3-5 2023. It focuses on the latest research developments in aeronautical applications, avionics systems, advanced aerodynamics, atmospheric chemistry, emerging technologies, safety management, unmanned aerial vehicles, and industrial applications. This book offers the state of the art of notable advances in engineering technologies and aviation applications and serves as an excellent source of reference for researchers and graduate students.
| Author | : Nikolaos Bourbakis |
| Publisher | : Springer Nature |
| Total Pages | : 439 |
| Release | : |
| Genre | : |
| ISBN | : 303167426X |
| Author | : Sharvari Tamane |
| Publisher | : Springer Nature |
| Total Pages | : 1027 |
| Release | : 2023-05-01 |
| Genre | : Computers |
| ISBN | : 9464631368 |
This is an open access book. As on date, huge volumes of data are being generated through sensors, satellites, and simulators. Modern research on data analytics and its applications reveal that several algorithms are being designed and developed to process these datasets, either through the use of sequential and parallel processes. In the current scenario of Industry 4.0, data analytics, artificial intelligence and machine learning are being used to support decisions in space and time. Further, the availability of Graphical Processing Units (GPUs) and Tensor Processing Units (TPUs) have enabled to processing of these datasets. Some of the applications of Artificial Intelligence, Machine Learning and Data Analytics are in the domains of Agriculture, Climate Change, Disaster Prediction, Automation in Manufacturing, Intelligent Transportation Systems, Health Care, Retail, Stock Market, Fashion Design, etc. The international conference on Applications of Machine Intelligence and Data Analytics aims to bring together faculty members, researchers, scientists, and industry people on a common platform to exchange ideas, algorithms, knowledge based on processing hardware and their respective application programming interfaces (APIs).
