REMOTE DIAGNOSTIC TECHNOLOGIES FOR ELECTRIC MOTORS IN MINING HAULAGE TRANSPORT
DOI:
https://doi.org/10.55640/Keywords:
Remote diagnostics; electric motors; mining haulage transport; predictive maintenance; real-time monitoring; IoT sensors; vibration analysis; thermal diagnostics; motor current signature analysis (MCSA); cloud analytics; digital twins; machine learning algorithms; smart mining; equipment reliability; condition-based maintenance (CBM); fault detection; operational efficiency.Abstract
The rapid digital transformation of the mining industry has accelerated the adoption of intelligent monitoring and diagnostic systems for heavy-duty haulage machinery. Electric motors used in mining haul trucks, locomotives, and auxiliary transport face extreme operational conditions, including high dust concentrations, fluctuating thermal loads, severe mechanical vibrations, and variable torque demands. These factors create an urgent need for remote diagnostic technologies capable of monitoring equipment health in real time and predicting potential failures before they disrupt productivity. This study examines the architecture, functional principles, and performance potential of remote diagnostic systems designed specifically for electric motors in mining haulage transport. The research highlights the synergy between IoT sensors, edge computing modules, cloud-based analytical platforms, and AI-powered condition-monitoring algorithms. Special emphasis is placed on vibration analytics, stator–rotor current signature analysis (MCSA), thermal anomaly detection, torque profile monitoring, and machine-learning-based failure prediction.
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