A Data-Driven Fault Detectability Analysis Approach Based on Actuator and Sensor Performance

This paper proposes a data-driven method for analyzing the detectability of faults in dynamic systems considering actuator and sensor performance. Firstly, the data-driven residuals are generated using orthogonal factorization and singular value decomposition (SVD) methods. The dynamics of the residuals are then characterized by mean vectors and covariance matrices, which are used to assess the difficulty of distinguishing between fault-free and faulty scenarios. Additionally, the performance evaluation indices of actuators and sensors are introduced, and a bank of performance-based quantitative indices is further employed to evaluate the detectability of various faulty scenarios. The effectiveness of our approach is demonstrated through a case study involving an unmanned aerial vehicle (UAV).