A Digital Twin for Brake Wear Predictive Maintenance

Brake pad wear is governed by coupled thermo-mechanical interactions in which degradation alters braking behaviour. This altered braking behaviour, in turn, affects vehicle dynamics, which, in turn, influences future wear evolution. Existing diagnostic and prognostic approaches typically neglect this dynamics–wear coupling, potentially limiting their ability to accurately predict remaining useful life under evolving operating conditions. This work proposes a digital-twin framework for brake wear prognosis that integrates state estimation, parameter adaptation, and dynamics-aware degradation modelling. The approach combines brake pad volume estimation from vehicle operational data, online identification of the brake wear coefficient through inverse modelling, and forward propagation of degradation using a wear-dependent dynamics model. The proposed digital-twin predictive maintenance framework is evaluated using simulated run-to-failure datasets for a mining load-haul-dumper and compared against data-driven and physics-based predictive maintenance models. Results show that the digital-twin approach improves the accuracy and stability of remaining useful life prediction, particularly under anomalous degradation conditions, and achieves earlier convergence to practically useful predictions. These findings demonstrate that accurate brake wear prognosis requires integrating degradation modelling with vehicle dynamics and online parameter updating. The proposed digital twin provides a practical pathway towards more reliable predictive maintenance in systems where degradation and system behaviour are strongly coupled.