Abstract:
This PhD thesis aims to study ontology-based AR content-related methods and
their impact in knowledge transfer, capture and re-use for cost-effective human
knowledge integration in digital diagnostic systems. Industry 4.0 has revealed the
importance of maintainers’ knowledge capture and re-use in diagnostics systems
for providing satisfactory solutions in cases where those systems cannot (e.g. nofault-found). Augmented Reality (AR) utilises content-related techniques to
transfer knowledge to maintainers for improving efficiency and effectiveness of
diagnosis tasks. Academic literature has shown that AR can also be utilised for
knowledge capture and re-use, but this has only been demonstrated in simple,
step-by-step repair operations. In diagnosis research, ontology-based methods are
applied to capture and re-use knowledge from unstructured and heterogenous
sources like humans. Nevertheless, these methods have not made use of AR
potential to contextualise knowledge and so, improve efficiency and effectiveness
of knowledge capture and re-use diagnosis operations...[cont.]
