A robust influence matrix approach to fault diagnosis

A robust scheme is proposed to detect faults, isolate them, and estimate their severity. The feature vector, which is a vector formed of the coefficients of the system transfer function, is estimated using a robust two-stage identification scheme: 1) a higher-order model is estimated using a singular value decomposition-based batch least-squares algorithm; and 2) a reduced-order model is derived by filtering-out the noise artifacts. The system is decomposed into functional units characterized by physical parameters. The influence of these physical parameters on the feature vector is captured in a vector termed the influence vector. The distance between, the inner product of the feature vector, and the influence vector are analyzed for diagnose faults. The proposed scheme is evaluated both on a simulated as well as an actual control system