Colloquium: Oscar Carpentier (ASM)

In the aerospace industry, traditional maintenance practices rely on time-scheduled servicing, often leading to unnecessary costs and inefficiencies as maintenance is performed without considering the actual condition of structures. This has driven growing interest in predictive maintenance (PdM), which uses prognostic health management (PHM) to optimize maintenance schedules based on real-time data. While significant progress has been made in prognostics for individual components, there is a gap in applying these techniques to larger, more complex aerospace structures. This thesis addresses that gap by focusing on upscaling prognostic models for aerospace structures. Specifically, it adapts the interoperability input-output model (IIM) to predict the remaining useful life (RUL) of higher-level structures using the training data of the lower-level components of that structure. Current research on prognostics primarily addresses low-level structures, such as individual coupons, with a limited focus on larger structures. To address this gap, the thesis investigates upscaling methods, specifically system-level prognostics (SLP), which considers a structure as a combination of interconnected components.

Supervisor: Nick Eleftheroglou