Digital Twin-Enabled Fault Detection for Suspension Systems in Autonomous Mining Haulage Vehicles

<div class="section abstract"><div class="htmlview paragraph">Autonomous vehicles for mining operations offer increased productivity, reduced total cost of ownership, decreased maintenance costs, improved reliability, and reduced operator exposure to harsh mining environments. A large flow of data exists between the remote operation and the ore haul vehicle, and part of the data becomes information for the maintenance sector which it monitors the operating conditions of various systems. One of the systems deserving attention is the suspension system, responsible for keeping the vehicle running and within a certain vibration condition to keep the asset operational and productive. Thus, this work aims to develop a digital twin-assisted system to evaluate the harmonic response of the vehicle’s body. Two representations were created based on equations of motion that modeled the oscillatory behavior of a mass-damper system. One of the representations indicates a quarter of the ore transport truck’s hydraulic system in a healthy state, called a virtual entity, and the other representation indicates a quarter of the same system prone to failure. Faults representing leakage in the hydraulic system chambers and piston seal loss are generated by changing the damping coefficient. A sensitivity analysis was conducted to evaluate the harmonic behavior of the vehicle body under a decrease in the damping coefficient. Finally, fault analysis in the hydraulic system was achieved through the calculation of residuals, which is the difference between the oscillatory response of the fault-prone system and the oscillatory response of the digital twin. The results demonstrate the effectiveness of the digital twin approach in accurately detecting and diagnosing faults within the suspension system, thereby ensuring the operational efficiency and sustainability of mining vehicles.</div></div>