LPV subspace identification for robust fault detection using a set-membership approach: Application to the wind turbine benchmark

This paper focuses on robust fault detection for Linear Parameter Varying (LPV) systems using a set-membership approach. Since most of models which represent real systems are subject to modeling errors, standard fault detection (FD) LPV methods should be extended to be robust against model uncertainty. To solve this robust FD problem, a set-membership approach based on an interval predictor is used considering a bounded description of the modeling uncertainty. Satisfactory results of the proposed approach have been obtained using several fault scenarios in the pitch subsystem considered in the wind turbine benchmark introduced in IFAC SAFEPROCESS 2009.Postprint (author's final draft

LPV subspace identification for robust fault detection using a set-membership approach: Application to the wind turbine benchmark

This paper focuses on robust fault detection for Linear Parameter Varying (LPV) systems using a set-membership approach. Since most of models which represent real systems are subject to modeling errors, standard fault detection (FD) LPV methods should be extended to be robust against model uncertainty. To solve this robust FD problem, a set-membership approach based on an interval predictor is used considering a bounded description of the modeling uncertainty. Satisfactory results of the proposed approach have been obtained using several fault scenarios in the pitch subsystem considered in the wind turbine benchmark introduced in IFAC SAFEPROCESS 2009