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The ARC Training Centre for
Transforming Maintenance through Data Science
Publications

Interpretable Survival Models for Predictive Maintenance

Paul Castle, Janet Ham, Melinda Hodkiewicz and Adriano Polpo

Relevant to the research in Theme 2. Predictive maintenance is a powerful tool to prevent unplanned failures. Paper discusses the interpretation of a common modelling approach (proportional hazards) from Bayesian and frequentist perspectives illustrating the benefits of the Bayesian approach for asset managers seeking to incorporate uncertainty more explicitly in their decisions about individual assets.


Publication: Research Publishing, Singapore
DOI: 978-981-14-8593-0