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Nonlinear Ultrasonics for Early Damage Detection
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Author(s): Rafael Munoz (Universidad de Granada, Spain), Guillermo Rus (Universidad de Granada, Spain), Nicolas Bochud (Universidad de Granada, Spain), Daniel J. Barnard (Iowa State University, USA), Juan Melchor (Universidad de Granada, Spain), Juan Chiachío Ruano (Universidad de Granada, Spain), Manuel Chiachío (Universidad de Granada, Spain), Sergio Cantero (Universidad de Granada, Spain), Antonio M. Callejas (Universidad de Granada, Spain), Laura M. Peralta (Universidad de Granada, Spain)and Leonard J. Bond (Iowa State University, USA)
Copyright: 2020
Pages: 36
Source title:
Virtual and Mobile Healthcare: Breakthroughs in Research and Practice
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-9863-3.ch034
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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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