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Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis

Mooring systems are an integral and sophisticated component of offshore assets and are subject to harsh conditions and cyclic loading. The early detection and characterisation of fatigue crack growth remain a crucial challenge. The scope of the present work was to establish filtering and alarm crite...

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Detalles Bibliográficos
Autores principales: Angulo, Ángela, Mares, Cristinel, Gan, Tat-Hean
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8348502/
https://www.ncbi.nlm.nih.gov/pubmed/34372266
http://dx.doi.org/10.3390/s21155030
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author Angulo, Ángela
Mares, Cristinel
Gan, Tat-Hean
author_facet Angulo, Ángela
Mares, Cristinel
Gan, Tat-Hean
author_sort Angulo, Ángela
collection PubMed
description Mooring systems are an integral and sophisticated component of offshore assets and are subject to harsh conditions and cyclic loading. The early detection and characterisation of fatigue crack growth remain a crucial challenge. The scope of the present work was to establish filtering and alarm criteria for different crack growth stages by evaluating the recorded signals and their features. The analysis and definition of parametrical limits, and the correlation of their characteristics with the crack, helped to identify approaches to discriminate between noise, initiation, and growth-related signals. Based on these, a filtering criterion was established, to support the identification of the different growth stages and noise with the aim to provide early warnings of potential damage.
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spelling pubmed-83485022021-08-08 Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis Angulo, Ángela Mares, Cristinel Gan, Tat-Hean Sensors (Basel) Article Mooring systems are an integral and sophisticated component of offshore assets and are subject to harsh conditions and cyclic loading. The early detection and characterisation of fatigue crack growth remain a crucial challenge. The scope of the present work was to establish filtering and alarm criteria for different crack growth stages by evaluating the recorded signals and their features. The analysis and definition of parametrical limits, and the correlation of their characteristics with the crack, helped to identify approaches to discriminate between noise, initiation, and growth-related signals. Based on these, a filtering criterion was established, to support the identification of the different growth stages and noise with the aim to provide early warnings of potential damage. MDPI 2021-07-24 /pmc/articles/PMC8348502/ /pubmed/34372266 http://dx.doi.org/10.3390/s21155030 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Angulo, Ángela
Mares, Cristinel
Gan, Tat-Hean
Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis
title Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis
title_full Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis
title_fullStr Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis
title_full_unstemmed Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis
title_short Diagnostic Feature Extraction and Filtering Criterion for Fatigue Crack Growth Using High Frequency Parametrical Analysis
title_sort diagnostic feature extraction and filtering criterion for fatigue crack growth using high frequency parametrical analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8348502/
https://www.ncbi.nlm.nih.gov/pubmed/34372266
http://dx.doi.org/10.3390/s21155030
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