activity
20202026
most citedDamage detection in operational wind turbine blades using a new approach based on machine learning

87 citations · 379 across the 17 of their papers we have counts for

collaborators
Showing 2023Show all

5 papers · 1 filter

cs.LG2023

Population-based wind farm monitoring based on a spatial autoregressive approach

W. Lin, K. Worden, E. J. Cross

An important challenge faced by wind farm operators is to reduce operation and maintenance cost. Structural health monitoring provides a means of cost reduction through minimising…

cs.LG2023

Sharing Information Between Machine Tools to Improve Surface Finish Forecasting

Daniel R. Clarkson, Lawrence A. Bull, Tina A. Dardeno +6

At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental cos…

cs.LG2023★ 1 cited

A spectrum of physics-informed Gaussian processes for regression in engineering

Elizabeth J Cross, Timothy J Rogers, Daniel J Pitchforth +2

Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven…

stat.ML2023★ 2 cited

Meta-models for transfer learning in source localisation

Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross +2

In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent…

stat.AP2023★ 27 cited

A probabilistic approach for acoustic emission based monitoring techniques: with application to structural health monitoring

C. A. Lindley, M. R. Jones, T. J. Rogers +4

It has been demonstrated that acoustic-emission (AE), inspection of structures can offer advantages over other types of monitoring techniques in the detection of damage; namely, an…