On the application of kernelised Bayesian transfer learning to population-based structural health monitoring

Abstract

Data-driven approaches to Structural Health Monitoring (SHM) generally suffer from a lack of available health-state data. In particular, for most structures, it is not possible to obtain a comprehensive set of labelled damage data – even covering the most common damage types – due to impracticalities and economic considerations in observing the structure in a range of damage states. One solution to this problem is to utilise labelled data from a set of ‘similar’ structures. The assumption is that, as a population, the group may have a shared label set that covers a wider range of damage states, which can be used in labelling a different structure of interest. These goals, producing a model that generalises for a population of structures, and transferring label information between structures, are part of a population-based view of SHM — known as population-based SHM (PBSHM). By considering data from a population, it is possible to make data-driven SHM practical in industrial contexts beyond unsupervised learning, i.e. novelty detection. In order to realise the potential of PBSHM, this paper applies a heterogeneous transfer learning method – kernelised Bayesian transfer learning (KBTL) – which is a sparse Bayesian method that infers a discriminative classifier from inconsistent and heterogeneous feature data, i.e. the dataset from each member of the population may refer to different quantities in different dimensions. The technique infers a shared latent space where data from each member of the population are mapped on top of each other, meaning a single classifier can jointly be inferred that generalises to the complete population. As a consequence, label information can be transferred in this shared latent space between members of the population. The ability to infer a mapping from inconsistent and heterogeneous feature data make the approach a heterogeneous transfer learning method. To the best of the authors knowledge, this is the first time a heterogeneous transfer learning method has been applied in an SHM context.