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Data-Driven Technology for Engineering Systems Health Management: Design Approach, Feature Construction, Fault Diagnosis, Prognosis, Fusion and Decisions

Autor Gang Niu
en Limba Engleză Hardback – 8 aug 2016
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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Specificații

ISBN-13: 9789811020315
ISBN-10: 9811020310
Pagini: 452
Ilustrații: XIII, 357 p. 204 illus.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.69 kg
Ediția:1st ed. 2017
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore

Cuprins

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.

Notă biografică




Textul de pe ultima copertă

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.

Caracteristici

Covers regular expressions usage in feature-based strategy and methodology for PHM/CBM Offers an in-depth review and explanations of algorithms for data-driven PHM/CBM Includes handy quick-reference tables to help readers with regular expression syntax Includes supplementary material: sn.pub/extras