One of the main sources of renewable energy is wind, which generates tremendous power while also reducing the need for greenhouse gas-emitting traditional power sources such as hydrocarbons and coal. However, many wind turbines installed in the early 2000's are nearing the end of their lifespan, and the problem remains of how to maintain, reduce, or decommission these aging turbines in a cost efficient way. In this talk we describe a digital twin for damage detection and maintenance scheduling of wind turbines which can track the condition of a wind turbine under different operating conditions. A key concern for wind energy that contributes to power production losses and high maintenance costs is deterioration of the turbine blades over time from environmental stressors such as lighting strikes, icing and accumulation of airborne particles which can result in leading edge erosion of the blades. We will discuss surrogate modeling of the turbines and classification of levels of leading edge erosion via machine learning.