Machine learning and knowledge discovery for engineering systems health management [electronic resource] / edited by Ashok N. Srivastava, Jiawei Han
- 其他作者：
- 其他題名：
- Chapman & Hall/CRC data mining and knowledge discovery series
- 出版： Boca Raton, FL : CRC Press c2011
- 叢書名： Chapman & Hall/CRC data mining and knowledge discovery series
- 主題： System failures (Engineering)--Prevention--Data processing , Machine learning.
- ISBN： 9781439841792 (electronic bk) 、 1439841799 (electronic bk)
- URL：
電子書
- 一般註：Print version record
- 書目註：Includes bibliographical references and index
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讀者標籤：
- 系統號： 005215366 | 機讀編目格式
館藏資訊
This volume presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. It emphasizes the importance of these techniques in managing the intricate interactions within and between engineering systems to maintain a high degree of reliability. Reflecting the interdisciplinary nature of the field, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management in application areas such as data centers, aircraft, and software systems.
摘要註
"Systems health is a broad multidisciplinary field of study that generates huge amounts of data and thus is an extremely appropriate forum in which to utilize machine learning and knowledge discovery techniques. This book explores the use of machine learning and knowledge discovery in systems health research. It covers data mining and text mining algorithms, anomaly detection, diagnostic and prognostic systems, and applications to engineering systems. Featuring contributions from leading experts, the book is the first to explore this emerging research area"--Provided by publisher "This book explores the development of state-of-the-art tools and techniques that can be used to automatically detect, diagnose, and in some cases, predict the effects of adverse events in an engineered system on its ultimate performance. This gives rise to the field Systems Health Management, in which methods are developed with the express purpose of monitoring the condition, or 'state of health' of a complex system, diagnosing faults, and estimating the remaining useful life of the system"--Provided by publisher
內容註
Section 1. Data-driven methods for systems health management -- section 2. Physics-based methods for systems health management -- section 3. Applications