The article presents a method for diagnosing cycloidal gear damage on a laboratory stand. The damage was simulated by removing the sliding sleeves from two adjacent external pins of the cycloidal gearbox. Damage to the sliding sleeves may occur under operating conditions and can lead to the destruction of the gear unit. Hence, early detection is essential. Signals from torque sensors, rotational speed sensors and vibration acceleration sensors of input and output shafts for various rotational speeds and transmission loads were recorded. The frequency analysis of these signals was carried out. Due to the fluctuation of the rotational speed, the frequency spectrum gives an approximate picture and is not useful in detecting this type of damage. The statistical characteristics of the signal were determined. However, only statistical moments of higher orders, such as kurtosis, are sensitive to the tested damage. Therefore, the use of multifractal analysis of the vibration signal using the wavelet leader method (WLMF) was considered. Then log-cumulants of the multifractal spectrum were selected as the new signal features.
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 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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