Population-Level Modelling for Truck Fleet Survival Analysis
View / Open Files
Conference Name
European Workshop on Structural Health Monitoring
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
Bull, L., Dhada, M., Steinert, O., Lindgren, T., Parlikad, A., & Girolami, M. Population-Level Modelling for Truck Fleet Survival Analysis. European Workshop on Structural Health Monitoring. https://doi.org/10.17863/CAM.82881
Abstract
Population-level modelling is used to address issues of data sparsity in the survival analysis of a simulated truck fleet. Specifically, hierarchical Bayes with mixed effects improves the predictive capability of hazard models. A set of correlated functions is learnt over the vehicle population – in a combined model – to approximate fleet predictors. Model uncertainty is reduced when sub-fleets of vehicles are allowed to share correlated information. In turn, vehicle groups with incomplete data (automatically) borrow statistical strength from data-rich groups.
Sponsorship
Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/W006022/1, particularly the Ecosystems of Digital Twins theme within that grant and The Alan Turing Institute.
Next Generation Converged Digital Infrastructure project (EP/R004935/1) funded by the Engineering and Physical Sciences Research Council and BT.
Embargo Lift Date
2023-03-28
Identifiers
External DOI: https://doi.org/10.17863/CAM.82881
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335452
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.