USE OF MACHINE LEARNING AND DYNAMIC MODELLING FOR CONDITION MONITORING IN MINING APPLICATIONS

2023

Travis Wiens

Machine learning (ML) and Artificial Intelligence (AI) show great promise for pattern detection in a number of fields. In this paper we present applications of these concepts to condition monitoring of mining processes, with a particular emphasis on using dynamic (time varying) data to evaluate system health. We present three examples: evaluating the integrity of underground potash mine roofs using the sound of scaling bar impacts, monitoring erosion of pipe walls using fluid wave propagation, and evaluating the wear of a hydraulic pump from its dynamic response to a change in load.
Keywords: Artificial intelligence, machine learning, dynamic systems, condition monitoring
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