DC FieldValueLanguage
dc.contributor.authorFritz, Henrieke-
dc.contributor.authorPeralta Abadia, Jose-
dc.contributor.authorLegatiuk, Dmitrii-
dc.contributor.authorSteiner, Maria-
dc.contributor.authorDragos, Kosmas-
dc.contributor.authorSmarsly, Kay-
dc.date.accessioned2021-11-08T09:18:40Z-
dc.date.available2021-11-08T09:18:40Z-
dc.date.issued2022-
dc.identifier.citationStructural Integrity 21: 143-164 (2022)de_DE
dc.identifier.issn2522-560Xde_DE
dc.identifier.urihttp://hdl.handle.net/11420/10804-
dc.description.abstractSmart structures leverage intelligent structural health monitoring (SHM) systems, which comprise sensors and processing units deployed to transform sensor data into decisions. Faulty sensors may compromise the reliability of SHM systems, causing data corruption, data loss, and erroneous judgment of structural conditions. Fault diagnosis (FD) of SHM systems encompasses the detection, isolation, identification, and accommodation of sensor faults, aiming to ensure the reliability of SHM systems. Typically, FD is based on “analytical redundancy,” utilizing correlated sensor data inherent to the SHM system. However, most analytical redundancy FD approaches neglect the fault identification step and are tailored to specific types of sensor data. In this chapter, an analytical redundancy FD approach for SHM systems is presented, coupling methods for processing any type of sensor data and two machine learning (ML) techniques, (i) an ML regression algorithm used for fault detection, fault isolation, and fault accommodation, and (ii) an ML classification algorithm used for fault identification. The FD approach is validated using an artificial neural network as ML regression algorithm and a convolutional neural network as ML classification algorithm. Validation is performed through a real-world SHM system in operation at a railway bridge. The results demonstrate the suitability of the FD approach for ensuring reliable SHM systems.en
dc.language.isoende_DE
dc.relation.ispartofStructural integrityde_DE
dc.subjectArtificial neural network (ANN)de_DE
dc.subjectConvolutional neural network (CNN)de_DE
dc.subjectFault diagnosis (FD)de_DE
dc.subjectMachine learning (ML)de_DE
dc.subjectSignal processingde_DE
dc.subjectStructural health monitoring (SHM)de_DE
dc.subjectWavelet transformde_DE
dc.subject.ddc620: Ingenieurwissenschaftende_DE
dc.titleFault Diagnosis in Structural Health Monitoring Systems Using Signal Processing and Machine Learning Techniquesde_DE
dc.typeArticlede_DE
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.abstract.englishSmart structures leverage intelligent structural health monitoring (SHM) systems, which comprise sensors and processing units deployed to transform sensor data into decisions. Faulty sensors may compromise the reliability of SHM systems, causing data corruption, data loss, and erroneous judgment of structural conditions. Fault diagnosis (FD) of SHM systems encompasses the detection, isolation, identification, and accommodation of sensor faults, aiming to ensure the reliability of SHM systems. Typically, FD is based on “analytical redundancy,” utilizing correlated sensor data inherent to the SHM system. However, most analytical redundancy FD approaches neglect the fault identification step and are tailored to specific types of sensor data. In this chapter, an analytical redundancy FD approach for SHM systems is presented, coupling methods for processing any type of sensor data and two machine learning (ML) techniques, (i) an ML regression algorithm used for fault detection, fault isolation, and fault accommodation, and (ii) an ML classification algorithm used for fault identification. The FD approach is validated using an artificial neural network as ML regression algorithm and a convolutional neural network as ML classification algorithm. Validation is performed through a real-world SHM system in operation at a railway bridge. The results demonstrate the suitability of the FD approach for ensuring reliable SHM systems.de_DE
tuhh.publisher.doi10.1007/978-3-030-81716-9_7-
tuhh.publication.instituteDigitales und autonomes Bauen B-1de_DE
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.volume21de_DE
tuhh.container.startpage143de_DE
tuhh.container.endpage164de_DE
dc.relation.projectBIM-basierte Informationsmodellierung zur semantischen Abbildung intelligenter Bauwerksmonitoringsystemede_DE
dc.relation.projectDatengestützte Analysemodelle für schlanke Bauwerke unter Nutzung von Explainable Artificial Intelligencede_DE
dc.relation.projectFehlertolerantes, drahtloses Bauwerksmonitoring basierend auf Frameanalyse und Deep Learningde_DE
dc.identifier.scopus2-s2.0-85117958533de_DE
local.status.inpressfalsede_DE
datacite.resourceTypeArticle-
datacite.resourceTypeGeneralJournalArticle-
item.creatorOrcidFritz, Henrieke-
item.creatorOrcidPeralta Abadia, Jose-
item.creatorOrcidLegatiuk, Dmitrii-
item.creatorOrcidSteiner, Maria-
item.creatorOrcidDragos, Kosmas-
item.creatorOrcidSmarsly, Kay-
item.grantfulltextnone-
item.creatorGNDFritz, Henrieke-
item.creatorGNDPeralta Abadia, Jose-
item.creatorGNDLegatiuk, Dmitrii-
item.creatorGNDSteiner, Maria-
item.creatorGNDDragos, Kosmas-
item.creatorGNDSmarsly, Kay-
item.mappedtypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderDeutsche Forschungsgemeinschaft (DFG)-
crisitem.project.funderid501100001659-
crisitem.project.funderid501100001659-
crisitem.project.funderid501100001659-
crisitem.project.funderrorid018mejw64-
crisitem.project.funderrorid018mejw64-
crisitem.project.funderrorid018mejw64-
crisitem.project.grantnoSM 281/12-1-
crisitem.project.grantnoSM 281/14-1-
crisitem.project.grantnoSM 281/15-1-
crisitem.author.deptDigitales und autonomes Bauen B-1-
crisitem.author.deptDigitales und autonomes Bauen B-1-
crisitem.author.deptDigitales und autonomes Bauen B-1-
crisitem.author.orcid0000-0003-0261-6792-
crisitem.author.orcid0000-0002-0028-5793-
crisitem.author.orcid0000-0002-4204-6547-
crisitem.author.orcid0000-0001-7228-3503-
crisitem.author.parentorgStudiendekanat Bauwesen (B)-
crisitem.author.parentorgStudiendekanat Bauwesen (B)-
crisitem.author.parentorgStudiendekanat Bauwesen (B)-
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