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Data fusion for multi-sensor nondestructive detection of surface cracks in ferromagnetic materials

  • Fatigue cracking is a dangerous and cost-intensive phenomenon that requires early detection. But at high test sensitivity, the abundance of false indications limits the reliability of conventional materials testing. This thesis exploits the diversity of physical principles that different nondestructive surface inspection methods offer, by applying data fusion techniques to increase the reliability of defect detection. The first main contribution are novel approaches for the fusion of NDT images. These surface scans are obtained from state-of-the-art inspection procedures in Eddy Current Testing, Thermal Testing and Magnetic Flux Leakage Testing. The implemented image fusion strategy demonstrates that simple algebraic fusion rules are sufficient for high performance, given adequate signal normalization. Data fusion reduces the rate of false positives is reduced by a factor of six over the best individual sensor at a 10 μm deep groove. Moreover, the utility of state-of-the-art imageFatigue cracking is a dangerous and cost-intensive phenomenon that requires early detection. But at high test sensitivity, the abundance of false indications limits the reliability of conventional materials testing. This thesis exploits the diversity of physical principles that different nondestructive surface inspection methods offer, by applying data fusion techniques to increase the reliability of defect detection. The first main contribution are novel approaches for the fusion of NDT images. These surface scans are obtained from state-of-the-art inspection procedures in Eddy Current Testing, Thermal Testing and Magnetic Flux Leakage Testing. The implemented image fusion strategy demonstrates that simple algebraic fusion rules are sufficient for high performance, given adequate signal normalization. Data fusion reduces the rate of false positives is reduced by a factor of six over the best individual sensor at a 10 μm deep groove. Moreover, the utility of state-of-the-art image representations, like the Shearlet domain, are explored. However, the theoretical advantages of such directional transforms are not attained in practice with the given data. Nevertheless, the benefit of fusion over single-sensor inspection is confirmed a second time. Furthermore, this work proposes novel techniques for fusion at a high level of signal abstraction. A kernel-based approach is introduced to integrate spatially scattered detection hypotheses. This method explicitly deals with registration errors that are unavoidable in practice. Surface discontinuities as shallow as 30 μm are reliably found by fusion, whereas the best individual sensor requires depths of 40–50 μm for successful detection. The experiment is replicated on a similar second test specimen. Practical guidelines are given at the end of the thesis, and the need for a data sharing initiative is stressed to promote future research on this topic.zeige mehrzeige weniger

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Metadaten
Autor*innen:René Heideklang
Dokumenttyp:Dissertation
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Jahr der Erstveröffentlichung:2018
Organisationseinheit der BAM:8 Zerstörungsfreie Prüfung
8 Zerstörungsfreie Prüfung / 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen
Veröffentlichende Institution:Bundesanstalt für Materialforschung und -prüfung (BAM)
Titel verleihende Institution:Humboldt-Universität zu Berlin
Gutachter*innen:Galina Ivanova, Ralf Reulke, Parisa Shokouhi
Datum der Abschlussprüfung:16.07.2018
Verlagsort:Berlin
Erste Seite:1
Letzte Seite:157
DDC-Klassifikation:Naturwissenschaften und Mathematik / Chemie / Analytische Chemie
Freie Schlagwörter:Data fusion; Multi-sensor; Non destructive testing; Surface cracks
Themenfelder/Aktivitätsfelder der BAM:Chemie und Prozesstechnik
DOI:10.18452/19586
URN:urn:nbn:de:kobv:b43-471339
Verfügbarkeit des Dokuments:Datei für die Öffentlichkeit verfügbar ("Open Access")
Lizenz (Deutsch):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
Datum der Freischaltung:04.01.2019
Referierte Publikation:Nein
Schriftenreihen ohne Nummerierung:BAM Dissertationen ohne Nummerierung
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