| Summary: | This thesis discusses approaches to evaluate the health of mining machinery, based on monitored vibration data. The objective was to develop a means to determine machine health, while operating on-line, without reference to an expert. This approach is based on processing acquired vibration data with artificial neural networks (ANN's). A case study, based on data obtained from the monitoring of locomotives at the Iron Ore Company (IOCC). Real time data patterns, profiles and trends, obtained by processing vibration signals from various points on locomotives, were used to test the developed technique. The results indicate that observed patterns and trends can be classified into categories that reliably indicate the mechanical state of the equipment. An implemented system will assist maintenance personnel at this mine to identify the trends of a developing component problem in advance of catastrophic failure. In addition the system will be able to predict its remaining life prior to catastrophic failure. Thus, a machine could be reliably and safely operated until just prior to failure of a component. The thesis work is a sub-component of a larger project at IOCC, to implement a mine-wide predictive/preventative maintenance program for pumps, locomotives, trucks, shovels and drills at their open-pit mine in Labrador City, Newfoundland. This system will use intermittent on- and off-line, condition monitoring based on ANNs and expert systems (ES). A functional overview is discussed. The data would identify where and what is the particular machine alarm condition. Such an approach would allow improved fault detection of machine components, especially in mines where trained personnel are not readily available. (Abstract shortened by UMI.)
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