In this paper, we develop data-driven method for the diagnosis of damage in mechanical structures using an array of distributed sensors. The proposed approach relies on comparing intrinsic geometry of data sets corresponding to the undamage and damage state of the system. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion map. The low dimensional embedding of the data set corresponding to different damage level is done using singular value decomposition of the diffusion map to identify the intrinsic geometry. We construct appropriate metric in diffusion space to compare the different data set corresponding to different damage cases. The application of this approach is demonstrated for damage diagnosis of wind turbine blades. Our simulation results show that the proposed diffusion map-based metric is not only able to distinguish the damage from undamage system state, but can also determine the extent and the location of the damage.