The method for the fault diagnosing of the air intake system of a gasoline engine, not detected by the onboard diagnostics system in a car, is described in this article. The aim is to detect and identify such faults like changes in sensor characteristic, faults of mass airflow measurement in the intake manifold or manifold leakages. These faults directly affect the air intake system performance that results in engine roughness and a power decrease. The method is based on the generation of residuals on the grounds of differences in indications of the manifold absolute pressure (MAP) and mass air flow (MAF) sensors installed in the car and the virtual, model-based sensors. The empirical model for the fault-free state was constructed at stationary operations of the engine. The residuals were then evaluated to classify the system health. Investigations were conducted for a conventional gasoline engine with port-fuel injection (PFI) and for a gasoline direct injection engine (GDI).
The article focuses on the fault not diagnosed by the OBD system. Apart from mechanical damages sensor faults are a serious group.
Although the sensor values are constantly monitored, some errors are not detected. The article presents a diagnostic model of the air
intake system of SI engine, which generates the control and work parameters of the engine for fault-free state. The parameters obtained
from the reference model can be compared with the parameters measured for the engine in any operating condition. Based on the model
the impact of sensor faults for other parameters was analyzed. Some errors can be masked by the adaptive control system of the engine,
which changes the parameters of the engine control. Simulation tests were verified on the test bench.
The work presents the investigations carried out on a spark-ignition internal combustion engine with gasoline direct injection. The tests were carried out under conditions of simulated damage to the air temperature sensor, engine coolant temperature sensor, fuel pressure sensor, air pressure sensor, intake manifold leakage, and air flow disturbances. The on-board diagnostic system did not detect any damage because the sensor indications were within acceptable limits. The engine control system in each case changed its settings according to the adaptive algorithm. Signal values in cycles from all available sensors in the engine control system and data available in the on-board diagnostic system of the car were recorded. A large amount of measurement data was obtained. They were used to create a statistical function that classifies sensor faults using an artificial neural network. A set of training data has been prepared accordingly. During learning the neural network, a hit rate of over 99% was achieved.
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