Time-based inspection and maintenance intervals are a conventional method of corrosion monitoring on aircraft. However, as corrosion processes do not necessarily occur in scheduled events, these conventional maintenance practices can lead to over- or under-estimation of costly inspections. A shift toward evidence-based and data driven predictive maintenance using a combination of on-asset monitoring devices and component-level models could improve efficiency and reduce total ownership costs. In particular, a “virtual sensor”, i.e., a trained model to predict the corrosion at a given location on the aircraft, can be leveraged to optimize the placement of physical real-time monitoring devices. This digital twin process can be applied to determine the corrosion susceptibility of a single aircraft, or to conduct a fleet-wide analysis. In this work, sensing device measurements deployed at varying locations will be used to demonstrate the applicability of severity tracking, through data-driven machine learning models. In particular, models will be trained on environmental parameters and leveraged to predict current (corrosion rate).