TY - THES A1 - Heideklang, René T1 - Data fusion for multi-sensor nondestructive detection of surface cracks in ferromagnetic materials N2 - 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 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. KW - Data fusion KW - Non destructive testing KW - Multi-sensor KW - Surface cracks PY - 2018 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-471339 DO - https://doi.org/10.18452/19586 SP - 1 EP - 157 CY - Berlin AN - OPUS4-47133 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -