Coverart for item
The Resource Data-driven technology for engineering systems health management : design approach, feature construction, fault diagnosis, prognosis, fusion and decisions, Gang Niu

Data-driven technology for engineering systems health management : design approach, feature construction, fault diagnosis, prognosis, fusion and decisions, Gang Niu

Label
Data-driven technology for engineering systems health management : design approach, feature construction, fault diagnosis, prognosis, fusion and decisions
Title
Data-driven technology for engineering systems health management
Title remainder
design approach, feature construction, fault diagnosis, prognosis, fusion and decisions
Statement of responsibility
Gang Niu
Creator
Subject
Language
eng
Summary
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
http://library.link/vocab/creatorName
Niu, Gang,
Dewey number
620/.00452
Illustrations
illustrations
Index
index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/subjectName
  • Fault location (Engineering)
  • Systems engineering
Label
Data-driven technology for engineering systems health management : design approach, feature construction, fault diagnosis, prognosis, fusion and decisions, Gang Niu
Instantiates
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Color
multicolored
Contents
  • Preface; Acknowledgements; Contents; 1 Background of Systems Health Management; 1.1 Introduction; 1.2 Maintenance Strategy; 1.3 From Maintenance to PHM; 1.4 Definitions and Terms of Systems Health Management; 1.5 Preface to Book Chapters; References; 2 Design Approach for Systems Health Management; 2.1 Introduction; 2.2 Systems Engineering; 2.3 Systems Engineering, Dependability, and Health Management; 2.4 SHM Lifecycle Stages; 2.4.1 Research Stage; 2.4.2 Requirements Development Stage; 2.4.3 System/Functional Analysis; 2.4.4 Design, Synthesis, and Integration
  • 2.4.5 System Test and Evaluation2.4.6 HM System Maturation; 2.5 A Systems-Based Methodology for PHM/CBM Design; 2.6 A Proposed PHM Design Approach for Rotary Machinery Systems; References; 3 Overview of Data-Driven PHM; 3.1 Introduction; 3.2 PHM Technical Approaches; 3.3 Data-Driven PHM/CBM System Architecture; 3.4 Role of Condition Monitoring, Fault Diagnosis, and Prognosis; 3.5 Fault Diagnosis Framework; 3.6 Problems During Implementation; 3.7 Related Techniques; References; 4 Data Acquisition and Preprocessing; 4.1 Introduction; 4.2 Data Acquisition; 4.2.1 Selecting a Proper Measure
  • 4.2.2 Vibration Transducers4.2.3 Transducer Selection; 4.2.4 Transducer Mounting; 4.2.5 Transducer Location; 4.2.6 Frequency Span; 4.2.7 Data Display; 4.3 Data Processing; 4.4 Data Analysis; 4.4.1 Features in Time Domain; 4.4.2 Features in Frequency Domain; 4.4.3 Features in Time-Frequency Domain; References; 5 Statistic Feature Extraction; 5.1 Introduction; 5.2 Basic Concepts; 5.2.1 Pattern and Feature Vector; 5.2.2 Class; 5.3 Parameter Evaluation Technique; 5.4 Principal Component Analysis (PCA); 5.5 Independent Component Analysis (ICA); 5.6 Kernel PCA; 5.7 Kernel ICA
  • 5.8 Fisher Discriminant Analysis (FDA)5.9 Linear Discriminant Analysis (LDA); 5.10 Generalized Discriminant Analysis (GDA); 5.11 Clustering; 5.11.1 k-Centers Clustering; 5.11.2 k-Means Clustering; 5.11.3 Hierarchical Clustering; 5.12 Other Techniques; References; 6 Feature Selection Optimization; 6.1 Introduction; 6.2 Individual Feature Evaluation (IFE); 6.3 Conditional Entropy; 6.4 Backward Feature Selection; 6.5 Forward Feature Selection; 6.6 Branch and Bound Feature Selection; 6.7 Plus l-Take Away r Feature Selection; 6.8 Floating Forward Feature Selection
  • 6.9 Distance-Based Evaluation Technique6.10 Taguchi Method-Based Feature Selection; 6.11 Genetic Algorithm; 6.11.1 General Concept; 6.11.2 Differences from Other Traditional Methods; 6.11.3 Simple Genetic Algorithm (SGA); 6.11.4 Feature Selection Using GA; 6.12 Summary; References; 7 Intelligent Fault Diagnosis Methodology; 7.1 Introduction; 7.2 Linear Classifier; 7.2.1 Linear Separation of Finite Set of Vectors; 7.2.2 Perceptron Algorithm; 7.2.3 Kozinec's Algorithm; 7.2.4 Multi-class Linear Classifier; 7.3 Quadratic Classifier; 7.4 Bayesian Classifier; 7.5 k-Nearest Neighbors (k-NN)
Control code
ocn954214872
Dimensions
unknown
Extent
1 online resource (xiii, 357 pages)
File format
unknown
Form of item
online
Isbn
9789811020315
Level of compression
unknown
Other physical details
illustrations
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)954214872
Label
Data-driven technology for engineering systems health management : design approach, feature construction, fault diagnosis, prognosis, fusion and decisions, Gang Niu
Publication
Copyright
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Color
multicolored
Contents
  • Preface; Acknowledgements; Contents; 1 Background of Systems Health Management; 1.1 Introduction; 1.2 Maintenance Strategy; 1.3 From Maintenance to PHM; 1.4 Definitions and Terms of Systems Health Management; 1.5 Preface to Book Chapters; References; 2 Design Approach for Systems Health Management; 2.1 Introduction; 2.2 Systems Engineering; 2.3 Systems Engineering, Dependability, and Health Management; 2.4 SHM Lifecycle Stages; 2.4.1 Research Stage; 2.4.2 Requirements Development Stage; 2.4.3 System/Functional Analysis; 2.4.4 Design, Synthesis, and Integration
  • 2.4.5 System Test and Evaluation2.4.6 HM System Maturation; 2.5 A Systems-Based Methodology for PHM/CBM Design; 2.6 A Proposed PHM Design Approach for Rotary Machinery Systems; References; 3 Overview of Data-Driven PHM; 3.1 Introduction; 3.2 PHM Technical Approaches; 3.3 Data-Driven PHM/CBM System Architecture; 3.4 Role of Condition Monitoring, Fault Diagnosis, and Prognosis; 3.5 Fault Diagnosis Framework; 3.6 Problems During Implementation; 3.7 Related Techniques; References; 4 Data Acquisition and Preprocessing; 4.1 Introduction; 4.2 Data Acquisition; 4.2.1 Selecting a Proper Measure
  • 4.2.2 Vibration Transducers4.2.3 Transducer Selection; 4.2.4 Transducer Mounting; 4.2.5 Transducer Location; 4.2.6 Frequency Span; 4.2.7 Data Display; 4.3 Data Processing; 4.4 Data Analysis; 4.4.1 Features in Time Domain; 4.4.2 Features in Frequency Domain; 4.4.3 Features in Time-Frequency Domain; References; 5 Statistic Feature Extraction; 5.1 Introduction; 5.2 Basic Concepts; 5.2.1 Pattern and Feature Vector; 5.2.2 Class; 5.3 Parameter Evaluation Technique; 5.4 Principal Component Analysis (PCA); 5.5 Independent Component Analysis (ICA); 5.6 Kernel PCA; 5.7 Kernel ICA
  • 5.8 Fisher Discriminant Analysis (FDA)5.9 Linear Discriminant Analysis (LDA); 5.10 Generalized Discriminant Analysis (GDA); 5.11 Clustering; 5.11.1 k-Centers Clustering; 5.11.2 k-Means Clustering; 5.11.3 Hierarchical Clustering; 5.12 Other Techniques; References; 6 Feature Selection Optimization; 6.1 Introduction; 6.2 Individual Feature Evaluation (IFE); 6.3 Conditional Entropy; 6.4 Backward Feature Selection; 6.5 Forward Feature Selection; 6.6 Branch and Bound Feature Selection; 6.7 Plus l-Take Away r Feature Selection; 6.8 Floating Forward Feature Selection
  • 6.9 Distance-Based Evaluation Technique6.10 Taguchi Method-Based Feature Selection; 6.11 Genetic Algorithm; 6.11.1 General Concept; 6.11.2 Differences from Other Traditional Methods; 6.11.3 Simple Genetic Algorithm (SGA); 6.11.4 Feature Selection Using GA; 6.12 Summary; References; 7 Intelligent Fault Diagnosis Methodology; 7.1 Introduction; 7.2 Linear Classifier; 7.2.1 Linear Separation of Finite Set of Vectors; 7.2.2 Perceptron Algorithm; 7.2.3 Kozinec's Algorithm; 7.2.4 Multi-class Linear Classifier; 7.3 Quadratic Classifier; 7.4 Bayesian Classifier; 7.5 k-Nearest Neighbors (k-NN)
Control code
ocn954214872
Dimensions
unknown
Extent
1 online resource (xiii, 357 pages)
File format
unknown
Form of item
online
Isbn
9789811020315
Level of compression
unknown
Other physical details
illustrations
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)954214872

Library Locations

    • InternetBorrow it
      Albany, Auckland, 0632, NZ
Processing Feedback ...