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Data-driven technology for engineeri...

Niu, Gang.

Data-driven technology for engineering systems health managementdesign approach, feature construction, fault diagnosis, prognosis, fusion and decisions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-driven technology for engineering systems health managementby Gang Niu.
Reminder of title:	design approach, feature construction, fault diagnosis, prognosis, fusion and decisions /
Author:	Niu, Gang.
Published:	Singapore :Springer Singapore :2017.
Description:	xiii, 357 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Fault location (Engineering)
Online resource:	http://dx.doi.org/10.1007/978-981-10-2032-2
ISBN:	9789811020322$q(electronic bk.)

Data-driven technology for engineering systems health managementdesign approach, feature construction, fault diagnosis, prognosis, fusion and decisions /
Niu, Gang.

Data-driven technology for engineering systems health managementdesign approach, feature construction, fault diagnosis, prognosis, fusion and decisions /[electronic resource] :by Gang Niu. - Singapore :Springer Singapore :2017. - xiii, 357 p. :ill., digital ;24 cm.

Background of Systems Health Management -- Design Approach for Systems Health Management -- Overview of Data-driven PHM -- Data Acquisition and Preprocessing -- Statistic Feature Extraction -- Feature Selection Optimization -- Intelligent Fault Diagnosis Methodology -- Science of Prognostics -- Data Fusion Strategy -- System Support and Logistics.

This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.

ISBN: 9789811020322$q(electronic bk.)

Standard No.: 10.1007/978-981-10-2032-2doiSubjects--Topical Terms:

266155
Fault location (Engineering)

LC Class. No.: TA169.6

Dewey Class. No.: 620.00452

Data-driven technology for engineering systems health managementdesign approach, feature construction, fault diagnosis, prognosis, fusion and decisions /
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300 $a xiii, 357 p. : $b ill., digital ; $c 24 cm.
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520 $a This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.
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