Nonlinear Ultrasonics for Early Damage Detection

Rafael Muñoz, Guillermo Rus, Nicolas Bochud, Daniel J. Barnard, Juan Melchor, Juan Chiachio-Ruano, Manuel Chiachio Ruano, Sergio Cantero, Antonio M. Callejas, Laura M. Peralta Pereira, Leonard J. Bond

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

4 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationEmerging Design Solutions in Structural Health Monitoring Systems
EditorsDiego Alexander Tibaduiza Burgos, Luis Eduardo Mujica, Jose Rodelas
Pages171-206
Number of pages36
DOIs
Publication statusPublished - 2015

Keywords

  • structural health monitoring (SHM)
  • life cycle
  • industrial components
  • nonlinear ultrasonics
  • Bayesian inverse problem
  • prognostics algorithms
  • early detection of damage

Fingerprint Dive into the research topics of 'Nonlinear Ultrasonics for Early Damage Detection'. Together they form a unique fingerprint.

Cite this