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A Damage Classification Approach for Structural Health Monitoring Using Machine Learning

Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection s...

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Published in:Complexity (New York, N.Y.) N.Y.), 2018-01, Vol.2018 (2018), p.1-14
Main Authors: Anaya, Maribel, Vitola, Jaime, Torres-Arredondo, Miguel Ángel, Tibaduiza, Diego, Pozo, Francesc
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description Inspection strategies with guided wave-based approaches give to structural health monitoring (SHM) applications several advantages, among them, the possibility of the use of real data from the structure which enables continuous monitoring and online damage identification. These kinds of inspection strategies are based on the fact that these waves can propagate over relatively long distances and are able to interact sensitively with and uniquely with different types of defects. The principal goal for SHM is oriented to the development of efficient methodologies to process these data and provide results associated with the different levels of the damage identification process. As a contribution, this work presents a damage detection and classification methodology which includes the use of data collected from a structure under different structural states by means of a piezoelectric sensor network taking advantage of the use of guided waves, hierarchical nonlinear principal component analysis (h-NLPCA), and machine learning. The methodology is evaluated and tested in two structures: (i) a carbon fibre reinforced polymer (CFRP) sandwich structure with some damages on the multilayered composite sandwich structure and (ii) a CFRP composite plate. Damages in the structures were intentionally produced to simulate different damage mechanisms, that is, delamination and cracking of the skin.
doi_str_mv 10.1155/2018/5081283
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source Wiley Open Access
subjects Algorithms
Analysis
Aprenentatge automàtic
Artificial intelligence
Carbon fiber reinforced plastics
Classification
Composite structures
Cracking (fracturing)
Damage detection
Enginyeria electrònica
Fiber composites
Fiber reinforced polymers
Fracture mechanics
Identification
Inspection
Machine learning
Methods
Nonlinear analysis
Pattern recognition
Piezoelectricity
Plates (structural members)
Principal components analysis
Resistència estructural
Sandwich structures
Sensors
Structural damage
Structural health monitoring
Wavelet transforms
Àrees temàtiques de la UPC
title A Damage Classification Approach for Structural Health Monitoring Using Machine Learning
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