Encoding Domain Expertise into Multilevel Models for Source Location
Lawrence A. Bull Matthew R. Jones Elizabeth J. Cross Andrew Duncan Mark Girolami
Data from populations of systems are prevalent in many industrial
applications. Machines and infrastructure are increasingly instrumented with
sensing systems, emitting streams of telemetry data with complex
interdependencies. In practice, data-centric monitoring procedures tend to
consider these assets (and respective models) as distinct -- operating in
isolation and associated with independent data. In contrast, this work captures
the statistical correlations and interdependencies between models of a group of
systems. Utilising a Bayesian multilevel approach, the value of data can be
extended, since the population can be considered as a whole, rather than
constituent parts. Most interestingly, domain expertise and knowledge of the
underlying physics can be encoded in the model at the system, subgroup, or
population level. We present an example of acoustic emission (time-of-arrival)
mapping for source location, to illustrate how multilevel models naturally lend
themselves to representing aggregate systems in engineering. In particular, we
focus on constraining the combined models with domain knowledge to enhance
transfer learning and enable further insights at the population level.