Introduction TO SHM

1. Introduction to SHM

• Motivation

• Levels of SHM strategy

• Statistical pattern recognition approaches

• Current methods: vibration-based SHM methods; wave-guided, electromechanical impedance, and others

• Challenges for real-world applications

2. Vibration-based damage detection

• Types of common damages in structures

• Extraction of features sensitive to damage: model-based versus data-based

• Classification of structural states by using supervised methods, semi-supervised, Reinforcement or non-supervised approaches

• Definition of thresholds: statistical approaches for hypothesis thesis

• Probability of false alarm: Type I error (false positive) and Type II error (false negative)

• Receiver operating characteristic (ROC) curve

3. Electromechanical impedance-based SHM

• Concepts of active structures

• Model of electromechanical coupling between a piezoelectric patch and a host structure

• Estimation of impedance signatures by spectral analysis

• Root means square deviation of the real and imaginary part of impedance signatures as a damage feature

• Temperature effects and compensation technique

4. An SHM approach using Machine Learning

• Novelty detection using Mahalanobis squared distance

• Fuzzy clustering for SHM with operational and environmental effects

• Gaussian process regression for damage quantification

• A Gaussian mixture model for damage detection

• Deep learning: convolutional neural network and recurrent neural networks

• Overview of SHM

• Methods

• Challenges for SHM: Nonlinearity and Uncertainties

• Fluctuations of features caused by environmental effects

• Types of common damages in structures - Corrosion and Cracks

• Types of common damages in structures - Loss of Tightening Torque in Bolted Joints

• Extraction of features sensitive to damage

• Classification of structural states by using supervised methods - Mahalanobis distance

• Definition of thresholds

• Probability of false alarm

• Receiver operating characteristic (ROC) curve

• Electromechanical impedance-based SHM

• Damage metrics for electromechanical impedance-based SHM

• Temperature effects and compensation techniques

• Transfer Component Analysis for Compensation of Temperature Effects

• Fuzzy clustering for damage detection

• Fuzzy c-means for SHM asssuming load effect

• Gaussian process regression for damage detection

• Gaussian process regression for compensation of temperature effects / damage quantification

• A Gaussian mixture model for damage detection

• Homework 1 - Statistical Classification using Mahalanobis Distance

• Homework 2 - Compensation of temperature effects

• Final Work:

The key idea is to implement a complete SHM approach to detect damage or quantify some data-driven information. The authors can use the approaches studied in the course or develop and combine other methods. It is fundamental to try to work on an original problem with suggestions to new directions.

The work can be performed individually or with two colleagues. The students are invited to use experimental data or simulated based on benchmarks to compare the performance.

It is demanded to include an in-depth performance analysis using ROC curves, confusion matrix, false-positive rate, and verification and validation of the method.

Write a detailed report with plots and all discussions about the method proposed.

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