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Methods for model-based fault detection are presented which detect a wide range of faults using only common production sensors, namely air mass sensor, manifold pressure sensor, manifold temperature sensor and engine speed. Five suitable reference models for fault detection are set up and identified at the test stand. The developed fault detection algorithms use the dependencies of the four sensor signals based on the reference models. Thereby five residuals and five symptoms are calculated. The model-based fault detection algorithms are implemented with a dSPACE Rapid Control Prototyping system and verified at the test stand. Measurements of online fault detection are shown.