Preventive and Predictive Maintenance for Heavy-duty Machinery
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Date
2022-05-16
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German Mongolian Institute for Resouce and Technology
Abstract
Research into the maintenance management of the mining industry and equipment maintenance is scant, despite the increased mining operation and productivity in recent years. In this paper the appropriate maintenance method for the subsystems of tracked dump truck and overall equipment is accomplished, by combining several maintenance analysis tools is. The maintenance data were processed using Pareto analysis technique, which assumes that 80% of a project's benefit may be realized by completing 20% of the work—or, conversely, that
80% of issues can be traced back to 20% of the causes. Pareto analysis is a used as a useful technique for evaluating quality and making decisions in this thesis. The target truck is
decomposed into subsystems and historical data is collected and analyzed using failure analysis tools to evaluate the right maintenance plan. Since the data is very broad, the more
accurate results can be achieved by breaking down the subsystem into smaller components level and analyzed. The results obtained from subsystem analysis proved that the proposed
method is applicable to the truck maintenance in component level.