Design of the Electronic Engine Control Unit Performance Test System of Aircraft

In this study, a real-time engine model and a test bench were developed to verify the performance of the EECU (electronic engine control unit) of a turbofan engine. The target engine is a DGEN 380 developed by the Price Induction company. The functional verification of the test bench was carried out using the developed test bench. An interface and interworking test between the test bench and the developed EECU was carried out. After establishing the verification test environments, the startup phase control logic of the developed EECU was verified using the real-time engine model which modeled the startup phase test data with SIMULINK. Finally, it was confirmed that the developed EECU can be used as a real-time engine model for the starting section of performance verification.

PARAMETER CLASSIFICATION SOFTWARE BASED ON CHARACTERIZERS AND KNOWLEDGE BASE FOR ELECTRONIC ENGINE CONTROL UNIT

The article discusses the issues of increasing the efficiency of the classification process of cards of electronic control units of a car engine. The analysis of the existing software for editing calibration tables in electronic engine control unit, which has tools for determining calibrations and data recognition, was carried out. The limits of use of such software products are conditioned by a small number of specified classes of calibration tables and low data processing speed. The analysis of testing results of classification methods using spectral decomposition demonstrated that a system based on this method requires complex transformations of the results of spectral decomposition. The use of spectral decomposition as a solution of the classification problem is possible if some characteristics of the input data are determined and used as data for classification. It was developed a data classification algorithm that uses characterizers to compute a clearly identified characteristic of the input matrix. The software package for the implementation of the developed algorithm was carried out by using the .NET Framework and the C # programming language. The testing of the classification system performance performed by using the developed software system on a small sample of maps. The results of preliminary testing showed that the system determines correctly the class of the provided card after training. Further testing on the Mercedes-Benz Bosch EDC16C31 / EDC16CP31 car block family showed that in cases of a large number of training images, the result meets the requirements. The performed tests allowed us to determine the optimal number of images for training and the time required for this.