Signal processing techniques for vibration-based health monitoring of smart structures

DOI10.1007/S11831-014-9135-7zbMath1348.94011OpenAlexW2035912441WikidataQ113323491 ScholiaQ113323491MaRDI QIDQ338763

Juan Pablo Amezquita-Sanchez, Hojjat Adeli

Publication date: 7 November 2016

Published in: Archives of Computational Methods in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11831-014-9135-7

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (6)

Combined Fourier-wavelet transforms for studying dynamic response of anisotropic multi-layered flexible pavement with linear-gradual interlayers ⋮ THREE-DIMENSIONAL ANIMATION NONLINEAR SYSTEM MODAL IDENTIFICATION USING WAVELET TRANSFORM ⋮ Structural Damage Detection Using Convolutional Neural Networks Based on Modal Strain Energy and Population of Structures ⋮ Damage identification algorithm of hinged joints for simply supported slab bridges based on modified hinge plate method and artificial bee colony algorithms ⋮ Damage detection of a cable-stayed bridge based on combining effective intrinsic mode functions of empirical mode decomposition using the feature selection technique ⋮ The vibration monitoring methods and signal processing techniques for structural health monitoring: a review

Uses Software

• ICALAB

Cites Work

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• Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool
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• Sensitivity analysis of crack detection in beams by wavelet technique
• Nonlinear time series. Nonparametric and parametric methods
• Wavelet packet based damage identification of beam structures
• Stochastic processes and filtering theory
• RETRACTED: A case study of damage detection in benchmark buildings using a Hilbert-Huang Transform-based method
• Pseudospectra, MUSIC, and dynamic wavelet neural network for damage detection of highrise buildings
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
• A new method for the nonlinear transformation of means and covariances in filters and estimators
• Empirical Wavelet Transform

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