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Machine learning and knowledge disco...

Han, Jiawei.

Machine learning and knowledge discovery for engineering systems health management

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning and knowledge discovery for engineering systems health managementedited by Ashok N. Srivastava, Jiawei Han.
其他作者:	Srivastava, Ashok N.
出版者:	Boca Raton, FL :CRC Press,©2011.
面頁冊數:	1 online resource (xxxvii, 464 p.) :ill.
標題:	System failures (Engineering)Prevention
電子資源:	http://www.crcnetbase.com/doi/book/10.1201/b11580
ISBN:	9781439841792 (electronic bk.)

Machine learning and knowledge discovery for engineering systems health management
Machine learning and knowledge discovery for engineering systems health management[electronic resource] /edited by Ashok N. Srivastava, Jiawei Han. - Boca Raton, FL :CRC Press,©2011. - 1 online resource (xxxvii, 464 p.) :ill. - Chapman & Hall/CRC data mining and knowledge discovery series. - Chapman & Hall/CRC data mining and knowledge discovery series..

Includes bibliographical references and index.

Section 1. Data-driven methods for systems health management -- section 2. Physics-based methods for systems health management -- section 3. Applications.

"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.

ISBN: 9781439841792 (electronic bk.)Subjects--Topical Terms:

696927
System failures (Engineering)
--Prevention

LC Class. No.: TA169.5 / .M33 2011

Dewey Class. No.: 620/.00452

Machine learning and knowledge discovery for engineering systems health management
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245 0 0 $a Machine learning and knowledge discovery for engineering systems health management $h [electronic resource] / $c edited by Ashok N. Srivastava, Jiawei Han.
260 $a Boca Raton, FL : $b CRC Press, $c ©2011.
300 $a 1 online resource (xxxvii, 464 p.) : $b ill.
490 1 $a Chapman & Hall/CRC data mining and knowledge discovery series
504 $a Includes bibliographical references and index.
505 0 $a Section 1. Data-driven methods for systems health management -- section 2. Physics-based methods for systems health management -- section 3. Applications.
520 $a "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.
520 $a "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.
588 0 $a Print version record.
650 0 $a System failures (Engineering) $x Prevention $x Data processing. $3 696927
650 0 $a Machine learning. $3 188639
700 1 $a Srivastava, Ashok N. $q (Ashok Narain), $d 1969- $3 696926
700 1 $a Han, Jiawei. $3 209106
830 0 $a Chapman & Hall/CRC data mining and knowledge discovery series. $3 359990
856 4 0 $u http://www.crcnetbase.com/doi/book/10.1201/b11580