Abstract
<div class="section abstract"><div class="htmlview paragraph">In the context of vehicular safety and performance, brake pads represent a critical component, ensuring controlled driving and accident prevention. These pads consist of friction materials that naturally degrade with usage, potentially leading to safety issues like delayed braking response and NVH disturbances. Unfortunately, assessing brake pad wear remains challenging for vehicle owners, as these components are typically inaccessible from the outside. Moreover, Indian OEMs have not yet integrated brake pad life estimation features. This research introduces a hybrid machine learning approach for predicting brake pad remaining useful life, comprising three modules: a weight module, utilizing mathematical formulations based on longitudinal vehicle dynamics to estimate vehicle weight necessary for calculating braking kinetic energy dissipation; and temperature and wear modules, employing deep neural networks for predictive modeling. Notably, the model’s training leverages rig-level data, with limited vehicle-level data for validation, achieving a validation accuracy of 94.8%. This innovative indirect approach holds the potential to be deployed universally in vehicles, enhancing safety without imposing additional burdens on customers or the environment.</div></div>