Data-Driven Technology for Engineering Systems Health Management

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.

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

Autorentext

Inhalt
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.