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Tim Pesch and Ryan Leadbetter present their research in the Data Analytics for Reseources Resources and Environments (DARE) seminar series.

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Firstly, the derivation of Maximum Likelihood Estimates (MLE’s) of the underpinning model parameters, and secondly, the introduction of a likelihood ratio test which can decide on whether components can be assumed identical. Both methods are powerful tools in reliability contexts. The former increases our understanding of component behaviour, especially upon failure of other components. This knowledge empowers system operators to make better decisions regarding maintenance schedules and failure time prediction. The latter supports operators in their quest of identifying component equivalence.

Seminar published on DARE YouTube channel

Conveyor Belt Wear Forecasting through a Bayesian Hierarchical Modeling Framework using Functional Data Analysis and Gamma Processes by Ryan Leadbetter

Reliability engineers make critical decisions about when and how to maintain conveyor belts, decisions that can significantly impact the production of the mine. The engineers use thickness measurements across the belt's width to justify these decisions. However, the current approaches to forecast the wear of the conveyor belts are naive and throw away valuable information about the special wear characteristics of the conveyor. We have developed a new method for forecasting belt wear that retains the wear profile's spatial structure and considers the wear rate's heterogeneity - caused by operation and ore body composition variations. 

Seminar published on DARE YouTube channel.

When - 21 February 2023 at 1pm AWST