A Robust time-varying Fault Detection and Isolation method for a Spark Injection Engine

In this paper we propose a Fault Detection and Isolation (FDI) filter design method for Spark Injection Engines. Starting from a detailed nonlinear Mean-value engine mathematical representation, a LPV approximation based on a judicious convex interpolation of a family of linearized models is obtained. An LPV-FDI filter based on the Luenberger observer theory is synthesized by ensuring guaranteed levels of disturbance rejection and fault detection and isolation. The resulting diagnostic filter is parameter-dependent and uses a set of scheduling engine parameters, assumed measurable on-line. The effectiveness of the LPV-FDI framework is illustrated by numerical examples. The obtained LPV approximation is here validated and the diagnostic capabilities of the proposed FDI architecture proved.