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Nonlinear Ultrasonics for Early Damage Detection

Nonlinear Ultrasonics for Early Damage Detection

Rafael Munoz, Guillermo Rus, Nicolas Bochud, Daniel J. Barnard, Juan Melchor, Juan Chiachío Ruano, Manuel Chiachío, Sergio Cantero, Antonio M. Callejas, Laura M. Peralta, Leonard J. Bond
ISBN13: 9781466684904|ISBN10: 1466684909|EISBN13: 9781466684911
DOI: 10.4018/978-1-4666-8490-4.ch009
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MLA

Munoz, Rafael, et al. "Nonlinear Ultrasonics for Early Damage Detection." Emerging Design Solutions in Structural Health Monitoring Systems, edited by Diego Alexander Tibaduiza Burgos, et al., IGI Global, 2015, pp. 171-206. https://doi.org/10.4018/978-1-4666-8490-4.ch009

APA

Munoz, R., Rus, G., Bochud, N., Barnard, D. J., Melchor, J., Ruano, J. C., Chiachío, M., Cantero, S., Callejas, A. M., Peralta, L. M., & Bond, L. J. (2015). Nonlinear Ultrasonics for Early Damage Detection. In D. Burgos, L. Mujica, & J. Rodellar (Eds.), Emerging Design Solutions in Structural Health Monitoring Systems (pp. 171-206). IGI Global. https://doi.org/10.4018/978-1-4666-8490-4.ch009

Chicago

Munoz, Rafael, et al. "Nonlinear Ultrasonics for Early Damage Detection." In Emerging Design Solutions in Structural Health Monitoring Systems, edited by Diego Alexander Tibaduiza Burgos, Luis Eduardo Mujica, and Jose Rodellar, 171-206. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8490-4.ch009

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Abstract

Structural Health Monitoring (SHM) is an emerging discipline that aims at improving the management of the life cycle of industrial components. The scope of this chapter is to present the integration of nonlinear ultrasonics with the Bayesian inverse problem as an appropriate tool to estimate the updated health state of a component taking into account the associated uncertainties. This updated information can be further used by prognostics algorithms to estimate the future damage stages. Nonlinear ultrasonics allows an early detection of damage moving forward the achievement of reliable predictions, while the inverse problem emerges as a rigorous method to extract the slight signature of early damage inside the experimental signals using theoretical models. The Bayesian version of the inverse problem allows measuring the underlying uncertainties, improving the prediction process. This chapter presents the fundamentals of nonlinear ultrasonics, their practical application for SHM, and the Bayesian inverse problem as a method to unveil damage and manage uncertainty.

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