Subspace-based fault detection methods are widely used for linear time-invariant systems. For linear time-periodic systems, those methods cannot be theoretically used, due to the intrinsic assumptions associated with those methods in the context of linear time-invariant models. Based on the approximation of time-periodic systems as time-invariant ones, those methods can still be applied and adapted to perform change detection for time-periodic systems, through a Gaussian residual built upon the identified modal parameters and their estimated variances. The proposed method is tested and validated on a small numerical model of a rotating wind turbine, with detection and isolation of a blade stiffness reduction leading to rotor anisotropy.