Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask Learning

TL;DR

This paper introduces a hierarchical Bayesian method for knowledge transfer across engineering fleets, leveraging domain expertise and multitask learning to improve predictive models despite data sparsity.

Contribution

It presents an interpretable hierarchical Bayesian framework that encodes domain knowledge and facilitates automatic information sharing among similar assets in engineering fleets.

Findings

Improved survival analysis for truck fleets.

Enhanced power prediction in wind farms.

Automatic knowledge transfer between data-rich and data-sparse groups.

Abstract

A population-level analysis is proposed to address data sparsity when building predictive models for engineering infrastructure. Utilising an interpretable hierarchical Bayesian approach and operational fleet data, domain expertise is naturally encoded (and appropriately shared) between different sub-groups, representing (i) use-type, (ii) component, or (iii) operating condition. Specifically, domain expertise is exploited to constrain the model via assumptions (and prior distributions) allowing the methodology to automatically share information between similar assets, improving the survival analysis of a truck fleet and power prediction in a wind farm. In each asset management example, a set of correlated functions is learnt over the fleet, in a combined inference, to learn a population model. Parameter estimation is improved when sub-fleets share correlated information at different levels of the hierarchy. In turn, groups with incomplete data automatically borrow statistical strength from those that are data-rich. The statistical correlations enable knowledge transfer via Bayesian transfer learning, and the correlations can be inspected to inform which assets share information for which effect (i.e. parameter). Both case studies demonstrate the wide applicability to practical infrastructure monitoring, since the approach is naturally adapted between interpretable fleet models of different in situ examples.