COURSE GOALS
Upon completion of this course, the student will be able to:
- Describe structural health monitoring as a problem in statistical pattern recognition
- Describe and classify the primary methods of structural health monitoring, with their associated advantages and disadvantages
- Describe the historical and current real-world applications of damage identification in the aerospace, civil, and mechanical engineering fields
- Discuss the primary practical implementation issues, including relevance of baseline measurements, importance of measurement statistics, and aspects of comparative studies between methods
COURSE OUTLINE
(Note: All course instruction in English only.
Course outline subject to change without prior notice)
1. Introduction
- Motivation for SHM, (NDE vs SHM)
- Statistical pattern recognition paradigm
- Fundamental axioms
- Historical overview: aerospace /civil/mechanical application
- Operational evaluation
2. Data Acquisition I
- Sensor network components- Sensor network paradigms
- Sensor fusion
- Excitation
3. Data Acquisition II
- Typical Sensors (accels, strain gages, fiber optic, PZT, acoustic)
- Telemetry/recording
- Power (energy harvesting)
- Emerging sensing technologies
4. Basic Statistical Tools
- Statistical moments/distributions
- Density estimation
- Multivariate analysis
- Principal component analysis
5. Introduction to Statistical Inference
- The need for statistical models in SHM
- Supervised vs unsupervised learning
- Group classification, regression modeling, outlier analysis
- Monte Carlo/bootstrap methods
6. Damage Sensitive Features I
- Feature selection criteria
- Feature vs metric
- Basic statistics
- Waveform/image comparisons
7. Damage Sensitive Features II
- Transition from linear to nonlinear response
- Physical model parameters
- Data-Based model parameters
- Residual errors from model predictions
8. Embedded sensing: MEMS
- Basic micro-fabrication process
- Various applications
- Markets for MEMS and microsystems
- MEMS accelerometers
9. Telemetry
- Embedded systems overview
- Autonomous intelligence
- Wireless communications
- System integration
10. Multi-functional Materials for SHM
- Enabling nanotechnologies
- Material characterization
- Examples of self-sensing materials
11. Embedded
Sensing: Piezoelectric Active Sensing & Guided Waves
- Introduction to
Piezoelectric Materials
- Lamb
wave theory
- Impedance methods
- Sensor self-diagnostics
12 Embedded Sensing: Fiber Optics
- Basic fiber optic sensing concepts (interferometry, multiplexing)
- Common sensing methods
- Performance comparison and discussion
-
Deployment examples (I-10, patrol boat)
13. Embedded Sensing: Acoustic Emissions
- Sensing systems
- Data analysis
- Performance comparison and discussion
- Applications to aerospace structures
14. Unsupervised Learning Methods
- Outlier analysis
- Statistical process control
- Projection techniques
15. Supervised Learning Methods
- Hypothesis testing
- Neural networks
- Support vector machines
- Regression analysis
16. Data Normalization
- Environmental/ operational effects on SHM
- Parametric modeling environmental effects
- Look-up table technique
- Machine learning techniques
17. Optimization SHM System Design and Sensor Placement
- Sensor optimization
- Bayesian risk analysis
- Detector design
- System design framework
18. Examples/Applications
- Validation data sets
- Aerospace applications
- Mechanical systems applications
- Civil
engineering applications
19. Software Demonstration
- Software overview
- Wave propagation software
- General purpose SHM software
- Validation data