Introduction TO SHM
Introduction to Structural Health Monitoring (SHM)
To present the fundamental ideas to implement modern structural health monitoring methods in mechanical, aeronautical, and civil structures.
Methology: The course is given through slideshows and a whiteboard with practical hands-on examples in Matlab and Python using data sets of various benchmarks proposed by SHM Lab (UNESP/Ilha Solteira) available in https://github.com/shm-unesp
Evaluation: The evaluation procedure will be performed through seminars and projects held by the students.
Topics: Introduction to SHM, Vibration-based damage detection, Electromechanical impedance-based SHM, an SHM approach using Machine Learning
Prerequisites: Vibration, Statistics, Signal Processing
summary
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)
Reiver 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
Root means square deviation of the real and imaginary part of impedance signatures as a damage feature
Estimation of impedance signatures by spectral analysis
Temperature effects and compensation technique
4. An SHM approach using Machine Learning
Novelty detection using Mahalanobis squared distance
A Gaussian mixture model for damage detection
Fuzzy clustering for SHM with operational and environmental effects
Gaussian process regression for damage quantification
Deep learning: convolutional neural network and recurrent neural networks
Assignments
References
Farrar, C.; Worden, K. Stuctural Health Monitoring: A Machine Learning, Wiley, UK, 2012.
Inman, D.; Farrar, C.; Lopes Jr, V; Steffen Jr, V. Damage Prognosis: For Aerospace, Civil and Mechanical Systems, Wiley, UK, 2007.
Paixão, J. A. S. ; da Silva, Samuel ; Figueiredo, Eloi ; Radu, L. ; Park, G. . Delamination area quantification in composite structures using Gaussian process regression and auto-regressive models. Journal of Vibration and Control, 2020. https://doi.org/10.1177%2F1077546320966183
da Silva, Samuel; Paixão, J. A. S. ; Rebillat, M. ; Mechbal, N.. Extrapolation of AR models using cubic splines for damage progression evaluation in composite structures. Journal of Intelligent Material Systems and Structures, 2020. https://doi.org/10.1177%2F1045389X20963171
Cano, W.; da Silva, Samuel . Fuzzy clustering and AR models for damage detection in CFRP coupons considering loading effect. Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 42, p. 241, 2020.
Villani, L. G. G.; da Silva, Samuel ; Cunha, Americo ; Todd, M. D . On the detection of a nonlinear damage in an uncertain nonlinear beam using stochastic Volterra series. Structural Health Monitoring, v. 19, p. 1137-1150, 2020.
Villani, L. G. G.; da Silva, Samuel ; Cunha Jr A. B. . Damage detection in uncertain nonlinear systems based on stochastic Volterra series. Mechanical Systems and Signal Processing, v. 125, p. 288-310, 2019.
Villani, L. G. G., ; da Silva, Samuel ; Cunha Jr, A. B. ; Todd, M. D. . Damage detection in an uncertain nonlinear beam based on stochastic Volterra series: an experimental application. Mechanical Systems and Signal Processing, v. 128, p. 463-478, 2019.
da Silva, Samuel. Data-driven model identification of guided wave propagation in composite structures. Journal of the Brazilian Society of Mechanical Sciences and Engineering, v. 40, p. 543, 2018
Shiki, S. B. ; DA SILVA, S. ; Todd, M. D. . On the application of discrete-time Volterra series for the damage detection problem in initially nonlinear systems. Structural Health Monitoring, v. 16, p. 62-78, 2017.
Gonsalez, C. G.; da Silva, Samuel ; Brennan, M. J. ; Lopes Jr, V. Structural damage detection in an aeronautical panel using analysis of variance. Mechanical Systems and Signal Processing, v. 52-53, p. 206-216, 2015.
da Silva, S.; Gonsalez, C. G. ; Lopes JR, V. . Adaptive filter feature identification for structural health monitoring in an aeronautical panel. Structural Health Monitoring, v. 10, p. 481-489, 2011.
Silva, S.; Dias Jr, M. ; Lopes Jr, V. ; Brennan, M. J. . Structural Damage Detection By Fuzzy Clustering. Mechanical Systems and Signal Processing, 22, p. 1636-1649, 2008.
Silva, S.; Dias Jr, M. ; Lopes Jr, V. Structural Health Monitoring in Smart Structures Through Time Series Analysis. Structural Health Monitoring, v. 7, p. 231-244, 2008.