Application of a Digital Twin Virtual Engineering Tool for Ground Vehicle Maintenance Forecasting 2022-01-0364

The integration of sensors, actuators, and real-time control in transportation systems enables intelligent system operation to minimize energy consumption and maximize occupant safety and vehicle reliability. The operating cycle of military ground vehicles can be on- and off-road in harsh weather and adversarial environments, which demands continuous subsystem functionality to fulfill missions. Onboard diagnostic systems can alert the operator of a degraded operation once established fault thresholds are exceeded. An opportunity exists to estimate vehicle maintenance needs using model-based predicted trends and eventually compiled information from fleet operating databases. A digital twin, created to virtually describe the dynamic behavior of a physical system using computer-mathematical models, can estimate the system behavior based on current and future operating scenarios while accounting for past effects. In this manner, the collection of real-time data of the physical vehicle can be compared with the virtual counterpart to assess the likelihood of degraded operation and recommend maintenance. This paper creates a digital twin for an off-road tracked ground vehicle with an accompanying parameter database. A modular architecture enables different design reconfigurations to evaluate various chassis subsystems and vehicle-ground mobility interfaces. A preventive maintenance algorithm is applied to monitor the operating behavior of the vehicle. The prediction tool, operating in parallel with the digital twin, may use model-based and sensory signal condition indicators to monitor vehicle performance. A case study investigates two operating scenarios: a virtual wheeled suspension system is degraded; and a virtual tracked vehicle experiences missing trackpads, which subtly alter the performances. The numerical results will offer insight into the pathway to begin creating a digital twin-based maintenance forecasting strategy.