Fault Diagnosis Based on Parameter-Identification and Hybrid State Estimation Applied to an Internal Combustion Engine

The fault identification in a scenario of multiple concurrent faults is studied for an internal combustion engine exposed to sensor, actuator and structural faults. The focus is on numerical feasible algorithms with fixed timespace behavior that are suitable for real-time application. Two methodologies are tested in simulation. First we show that a single extended Kalman Filter (eKF) can be used to identify multiple faults. The diagnosis is formulated as a parameter estimation problem, which is solved by augmenting the plant model with additional states that represent the faults, and the eKF estimates these states. Then we extend this single eKF by applying the Interacting Multiple Model (IMM) algorithm based on multiple eKFs that provides a well trusted estimation scheme for multi modal (hybrid) systems. Furthermore we analyze the capability to improve the estimation quality of the IMM by a complementary mode estimation scheme that is built through Analytic Redundancy Relations (ARR).