Data-Driven Technology for Engineering Systems Health Management
Data-Driven Technology for Engineering Systems Health Management
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
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 Evaluation
2.4.6 HM System Maturation
2.5 A Systems-Based Methodology for PHM/CBM Design
2.6 A Proposed PHM Design Approach for Rotary Machinery Systems
3 Overview of Data-Driven PHM
3.1 Introduction
3.2 PHM Technical Approaches
3.3 Data-Driven PHM/CBM System Architecture
The open system architecture for condition-based maintenance organization (OSA-CBM) has specified an open standard proposal on how a CBM system should be designed technically.
- Layer 1 Sensor module
- Layer 2 Signal processing
- Layer 3 Condition monitor
- Layer 4 Health assessment
- Layer 5 Prognosis
- Layer 6 Decision support
- Layer 7 Presentation
3.4 Role of Condition Monitoring, Fault Diagnosis, and Prognosis
3.5 Fault Diagnosis Framework
Fault Detection Frameworks
3.6 Problems During Implementation
3.7 Related Techniques
4 Data Acquisition and Preprocessing
4.1 Introduction
4.2 Data Acquisition
4.2.1 Selecting a Proper Measure
4.2.2 Vibration Transducers
4.2.3 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
Signal Processing
4.4 Data Analysis
4.4.1 Features in Time Domain
Signal Processing in the Time Domain
4.4.2 Features in Frequency Domain
Signal Processing in the Frequency Domain
4.4.3 Features in Time–Frequency Domain
5 Statistic Feature Extraction
Feature Reduction
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