Developing a data and knowledge management approach for Integrated vehicle health management.

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.