Subspace based Fault Detection and Identification for LPV Systems

This paper presents a new Fault detection and Identification approach Connected to Subspace Identification (FICSI) for a special class of nonlinear system casted to Linear but Parametrically Varying (LPV) form. The model based algorithm utilizes the specific nonlinear nature of the LPV system along the past horizon to construct the output predictor. Similar with its LTI counterpart in a companion paper ([Dong and Verhaegen (2008)]), the FICSI-LPV avoids projecting the residual vector onto the parity space of the extended observability matrix, and hence produces residuals more sensitive to faults than the parity space approach (PSA) for LPV systems does. Asymptotically unbiased condition and algorithm are also proposed for fault estimation. The difference of FICSI from the existing fault detection and estimation approaches based on PSA or moving horizon estimation (MHE) can also be attributed to the fact that FICSI does not require an LPV state space model, but a sequence of Markov parameters mapping the I/O measurements and the scheduling parameters to the residual, which can be estimated in closed-loop.