Inefficiencies in mine equipment maintenance processes result in high operation costs and reduce mine sustainability. However, current methods for process optimization are limited due to a lack of access to structured data. This research aims to test the hypothesis that process mining techniques can be used to optimize workflow for mine equipment maintenance processes using low-level data. This is achieved through a process-oriented analysis where low-level data are processed as an event log and used as input for a developed process model. We present a Discrete-Event Simulation of the maintenance process to generate an event log from low-level data and analyze the process with process mining. A case study of the maintenance process in an underground block caving mine is used to gain operational insight. The diagnosis of the mine's maintenance process showed a loss of 23,800 equipment operating hours per year, with a non-production cost of about 1.12 MUSD/year. Process mining obtained a non-biased representation of the maintenance process and aided in identifying bottlenecks and inefficiencies in the equipment maintenance processes.