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)

  • 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

  • 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

Lectures Notes

Assignments

  • Homework 1 - Statistical Classification using Mahalanobis Distance

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.