Sensor selection for fault parameter identification applied to an internal combustion engine

This paper presents a methodology for the selection of suitable sensors for a diagnosis problem that is relevant for control of internal combustion engines. The diagnosis is formulated as a parameter estimation problem, which is solved by augmenting the plant model with additional states that represent the faults. An Extended Kalman Filter is applied to estimate the states of the augmented model. The sensor selection is based on the classical concepts of observability as well as on the distinguishability analysis for the faults.