In Frontiers in robotics and AI
One of the major obstacles to the widespread uptake of data-based Structural Health Monitoring so far, has been the lack of damage-state data for the (mostly high-value) structures of interest. To address this issue, a methodology for sharing data and models between structures has been developed-Population-Based Structural Health Monitoring (PBSHM). PBSHM works on the principle that, if populations of structures are sufficiently similar, or share sections which can be considered similar, then data and models can be shared between them for use in diagnostic inference. The PBSHM methodology therefore relies on two key components: firstly, identifying whether structures are sufficiently similar for successful transfer of diagnostics; this is achieved by the use of an abstract representation of structures. Secondly, machine learning techniques are exploited to effectively transfer information between the structures in a way that improves damage detection and classification across the whole population. Although PBSHM has been conceived to deal with large and general classes of structures, much of the detailed developments presented so far have concerned bridges; the aim of this paper is to provide similarly detailed discussions in the aerospace context. The overview here will examine data transfer between aircraft components, as well as illustrating how one might construct an abstract representation of a full aircraft.
Brennan Daniel S, Gosliga Julian, Gardner Paul, Mills Robin S, Worden Keith
2022
aerospace engineering, data-based structural health monitoring, fleet-based monitoring, irreducible element modelling, knowledge transfer, machine learning, population-based structural health monitoring, transfer learning
