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dc.contributor.authorLeadbetter, Ryan K.
dc.contributor.supervisorAloke Phataken_US
dc.contributor.supervisorChris Aldrichen_US
dc.contributor.supervisorMichael Smallen_US
dc.date.accessioned2025-05-02T06:22:17Z
dc.date.available2025-05-02T06:22:17Z
dc.date.issued2025en_US
dc.identifier.urihttp://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.publisherCurtin Universityen_US
dc.titleBayesian Hierarchical Modelling of Equipment Reliability in Mining: A Pragmatic Approachen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Sciencesen_US
curtin.accessStatusOpen accessen_US
curtin.facultyScience and Engineeringen_US
curtin.contributor.orcidLeadbetter, Ryan K. []en_US


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