Population-Level Modelling for Truck Fleet Survival Analysis

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Authors
Bull, Lawrence
Dhada, Maharshi
Steinert, Olof
Lindgren, Tony
Parlikad, Ajith Kumar
Girolami, Mark
Conference Name
European Workshop on Structural Health Monitoring
Type
Conference Object
This Version
AM
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Citation
Bull, L., Dhada, M., Steinert, O., Lindgren, T., Parlikad, A. K., & 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
Rights
All Rights Reserved
Licence URL: http://www.rioxx.net/licenses/all-rights-reserved
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