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Faults in the intake and exhaust path of turbocharged common-rail diesel engines lead to an increase of emissions and to performance losses. Fault detection strategies based on plausibility checks, threshold based trend or limit checking of sensor data are not able to detect and isolate all faults appearing in the intake and exhaust path without increasing of the number of sensors. The need to minimize mass and reduce cost, including the number of sensors, while maintaining robust performance leads to higher application of models for intake and exhaust path components. Therefore a concept of model based fault detection with parity equations is considered. It contains the following parts: modeling, residual generation with parity equations using parallel nonlinear models, fault to symptom transformation with masking of residuals dependent on the operating point and limit violation checking of the residuals.

Faults in the intake and exhaust path of turbocharged common-rail Diesel engines can lead to an increase of emissions and performance losses. Standard fault detection strategies based on plausibility checks and trend checking of sensor data are not able to detect and isolate all faults appearing in the intake and exhaust path without employing additional sensors. By applying model based methods a limited sensor configuration can be used for fault detection. Therefore a model based fault diagnosis concept with parity equations is considered, [1]. In this contribution the fault diagnosis system, which comprises semi-physical thermodynamic turbocharger model, models of gas pressure in the intake and exhaust manifold, residual generation, residual to symptom transformation and fault diagnosis is presented.