Equipment health monitoring in complex systems

• Main Authors: King, Stephen P. (Author), Mills, Andrew R. (Author), Kadirkamanathan, Visakan, 1962- (Author), Clifton, David A. (Author)
• Format: Electronic eBook
• Language: English
• Published: Boston : Artech House, [2018]
• Series: Artech House computing library.
• Subjects: Systems engineering.; Structural health monitoring.; elektronické knihy; electronic books
• ISBN: 9781630814977; 1630814970; 1608079724; 9781608079728
• Physical Description: 1 online resource (ix, 208 pages)

Table of Contents:

• Machine generated contents note: 1. Introduction
• 1.1. Maintenance Strategies
• 1.2. Overview of Health Monitoring
• 1.3. Organization of Book Contents
• References
• 2. Systems Engineering for EHM
• 2.1. Introduction
• 2.2. Introduction to Systems Engineering
• 2.2.1. Systems Engineering Processes
• 2.2.2. Overview of Systems Engineering for EHM Design
• 2.2.3. Summary
• 2.3. EHM Design Intent
• 2.3.1. State the Problem: Failure Analysis and Management
• 2.3.2. Model the System: Approaches for Failure Modeling
• 2.3.3. Investigate Alternatives: Failure Models
• 2.3.4. Assess Performance: Case Study
• 2.4. EHM Functional Architecture Design
• 2.4.1. State the Problem: EHM Functional Architecture Design
• 2.4.2. Model the System: Function Modeling and Assessment
• 2.4.3. Investigate Alternatives: Tools for Functional Architecture Design
• 2.4.4. Assess Performance: Gas Turbine EHM Architecture Optimization
• 2.5. EHM Algorithm Design
• 2.5.1. State the Problem: Monitoring Algorithm Design Process
• 2.5.2. Model the System: Detailed Fault Mode Modeling
• 2.5.3. Investigate Alternatives: Development Approaches
• 2.5.4. Assess Performance: Algorithm Design Case Study
• 2.6. Conclusion
• References
• 3. The Need for Intelligent Diagnostics
• 3.1. Introduction
• 3.2. The Need for Intelligent Diagnostics
• 3.3. Overview of Machine Learning Capability
• 3.4. Proposed Health Monitoring Framework
• 3.4.1. Feature Extraction
• 3.4.2. Data Visualization
• 3.4.3. Model Construction
• 3.4.4. Definition of Model Boundaries
• 3.4.5. Verification of Model Performance
• References
• 4. Machine Learning for Health Monitoring
• 4.1. Introduction
• 4.2. Feature Extraction
• 4.3. Data Visualization
• 4.3.1. Principal Component Analysis
• 4.3.2. Kohonen Network
• 4.3.3. Sammon's Mapping
• 4.3.4. NeuroScale
• 4.4. Model Construction
• 4.5. Definition of Model Boundaries
• 4.6. Verification of Model Performance
• 4.6.1. Verification of Regression Models
• 4.6.2. Verification of Classification Models
• References
• 5. Case Studies of Medical Monitoring Systems
• 5.1. Introduction
• 5.2. Kernel Density Estimates
• 5.3. Extreme Value Statistics
• 5.3.1. Type-I EVT
• 5.3.2. Type-II EVT
• 5.3.3. Gaussian Processes
• 5.4. Advanced Methods
• References
• 6. Monitoring Aircraft Engines
• 6.1. Introduction
• 6.1.1. Aircraft Engines
• 6.1.2. Model-Based Monitoring Systems
• 6.2. Case Study
• 6.2.1. Aircraft Engine Air System Event Detection
• 6.2.2. Data and the Detection Problem
• 6.3. Kalman Filter-Based Detection
• 6.3.1. Kalman Filter Estimation
• 6.3.2. Kalman Filter Parameter Design
• 6.3.3. Change Detection and Threshold Selection
• 6.4. Multiple Model-Based Detection
• 6.4.1. Hypothesis Testing and Change Detection
• 6.4.2. Multiple Model Change Detection
• 6.5. Change Detection with Additional Signals
• 6.6. Summary
• References
• 7. Future Directions in Health Monitoring
• 7.1. Introduction
• 7.2. Emerging Developments Within Sensing Technology
• 7.2.1. Low-Cost and Ubiquitous Sensing
• 7.2.2. Ultra-Minaturization
• Nano and Quantum
• 7.2.3. Bio-Inspired
• 7.2.4. Summary
• 7.3. Sensor Informatics for Medical Monitoring
• 7.3.1. Deep Learning for Patient Monitoring
• 7.4. Big Data Analytics and Health Monitoring
• 7.5. Growth in Use of Digital Storage
• 7.5.1. Example Health Monitoring Application Utilizing Grid Capability
• 7.5.2. Cloud Alternatives
• References.