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商品詳細
Data-Driven Methods for Reliability and Safety Engineering: Applications in Industrial Systems: Leveraging AI, Machine Learning, and Advanced Analytics to Enhance Risk Assessment, Decision-Making, and System Performance.
・ISBN 978-3-032-22872-7 hard EUR 199.99
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| 著者・編者 | Li, He / Feng, Ke / Yazdi, Mohammad / Huang, Hong-Zhong (eds.), |
|---|---|
| シリーズ | (Springer Series in Reliability Engineering) |
| 出版社 | (Springer Nature Switzerland AG, SZ) |
| 出版年月 | 2026 |
| ページ数 | 728 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A05-62065＞ |
解説
This book provides a comprehensive guide to using data-driven methods in reliability and safety engineering for industrial systems. It explores how modern technologies like data analytics, machine learning, and artificial intelligence can enhance decision-making, predict failures, and improve system resilience.
In an era of increasingly complex industrial systems, traditional methods often fail to address reliability and safety challenges. This book highlights how integrating data-driven techniques can optimize system performance, reduce risks, and enhance safety outcomes. Key topics include predictive maintenance, risk assessment, AI integration, and the challenges of implementing these technologies in real-world environments. Case studies across industries like energy and manufacturing illustrate the practical applications of these methods.
This book is aimed at professionals in reliability engineering, safety, risk management, and industrial systems, as well as researchers and students seeking to understand the role of data-driven methods in modern engineering practices.