Data-Driven Technology for Engineering Systems Health Management [electronic resource] : Design Approach, Feature Construction, Fault Diagnosis, Prognosis, Fusion and Decisions /
Niu, Gang.author.
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.
Includes bibliographical references at the end of each chapters and index.
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.
Quality control.
Reliability.
Industrial safety.
Computer system failures.
Applied mathematics.
Engineering mathematics.
Data mining.
Pattern recognition.
Quality Control, Reliability, Safety and Risk.
System Performance and Evaluation.
Applications of Mathematics.
Data Mining and Knowledge Discovery.
Pattern Recognition.