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Data Analytics and Artificial Intelligence for Predictive Maintenance in Smart Manufacturing.
・ISBN 978-1-032-77016-1 paper GB£ 62.99
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| 著者・編者 | Kumar Tyagi, Amit / Tiwari, Shrikant / Soni, Gulshan (eds.), |
|---|---|
| シリーズ | (Advances in Intelligent Decision-Making, Systems Engineering, and Project Management) |
| 出版社 | (CRC Press, UK) |
| 出版年月 | 2026 |
| ページ数 | 400 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A05-77456＞ |
解説
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.