Abstract:
In Integrated Vehicle Management (IVHM), research and engineering activities
are conducted that generate large amounts of data and content. These activities
include simulations, observations, derivation, experiments and referencing.
However, IVHM still faces a range of data- and Knowledge Management (KM)
challenges ranging from data accuracy to long-term availability for prognostic and
diagnostic health management. IHVM is data-centric and therefore requires a
robust data life cycle management to supports its data- and Knowledge
Management activities. An understanding of the concept of KM is fundamental to
addressing the IVHM data and knowledge management issues.
In this regard, this thesis contextualises ‘Knowledge Management’ for IVHM by
attempting to resolve the intellectual paradox that has characterised it over the
years. It discusses the origins of Knowledge Management as a discipline and
addresses its historical inconsistencies. This review of KM and its origins serves
as a scoping study guiding a systematic review of data life cycle models. It
reviews relevant standards and their role in the data life cycle.
Guided by the V-Model, a Data Life Cycle Model is developed as a result and
validated using a multi-technique approach combining peer review and expert
insights obtained through a purposive survey. The model is then applied to IVHM
centre Knowledge Management System development (KMS). The outcome
includes an improved requirements gathering process and a solid foundation for
resolving IVHM data and Knowledge Management challenges.
