Bayesian Hierarchical Modelling of Equipment Reliability in Mining: A Pragmatic Approach
dc.contributor.author | Leadbetter, Ryan K. | |
dc.contributor.supervisor | Aloke Phatak | en_US |
dc.contributor.supervisor | Chris Aldrich | en_US |
dc.contributor.supervisor | Michael Small | en_US |
dc.date.accessioned | 2025-05-02T06:22:17Z | |
dc.date.available | 2025-05-02T06:22:17Z | |
dc.date.issued | 2025 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/97662 | |
dc.description.abstract |
This thesis developed and applied Bayesian reliability models for maintenance applications in the mining industry. New methods to address the novel problems in observational datasets from mining were developed, notably for Weibull analysis of partially observed lifetime data and modelling of small, noisy, and complex degradation signals with gamma stochastic process. Importantly, the thesis also provided concrete examples of a principled Bayesian model-building workflow in the reliability domain. | en_US |
dc.publisher | Curtin University | en_US |
dc.title | Bayesian Hierarchical Modelling of Equipment Reliability in Mining: A Pragmatic Approach | en_US |
dc.type | Thesis | en_US |
dcterms.educationLevel | PhD | en_US |
curtin.department | School of Electrical Engineering, Computing and Mathematical Sciences | en_US |
curtin.accessStatus | Open access | en_US |
curtin.faculty | Science and Engineering | en_US |
curtin.contributor.orcid | Leadbetter, Ryan K. [] | en_US |