This paper proposes a novel principal component analysis (PCA)-based sensor fault detection methodology for smart structures employing magnetorheological (MR) dampers. The MR damper is operated by a semiactive nonlinear fuzzy controller (SNFC) that is developed by integration of a set of Lyapunov optimal controllers, Kalman filters, and a semiactive converter with the fuzzy interpolation method. A numeric residual generator is found using the PCA analysis of ten measurements obtained from the structure-MR damper system for sensor fault detection. Using the matrix of this residual generator, the detectability and isolability of each sensor has been analyzed and the detection and isolation algorithm is applied to the smart structural system with different levels of artificially added faults. The simulation demonstrated that the proposed PCA-based sensor fault detection approach is effective in identifying the sensor faults of large smart structures employing MR dampers.