AUTOMATED MODELS OF SOFTWARE SYSTEMS FOR VEHICLE DIAGNOSTICS: APPROACH TO DATA INTEGRATION

DOI: 10.31673/2409-7292.2025.017899

Abstract

The introduction of the OBD-2 interface provided standardized access to vehicle diagnostic data, enabling realtime monitoring of the system’s status. Effective utilization of this data requires the development of intelligent algorithms
capable of analyzing the obtained information while considering signal variability and the probability of faults. One of
the approaches to improving analysis accuracy is the use of finite state machines (FSMs), which allow structuring the
decision-making process based on a set of defined states and transitions between them. This study explores the method
of integrating FSM into OBD-2 data analysis processes to create an automated diagnostic system that enhances fault
detection accuracy and reduces the number of false-positive results. The proposed diagnostic model employs FSMs to
build a flexible and scalable logic for analyzing a vehicle's condition. As part of the research, a mathematical FSM model
was developed, considering the temporal variation of OBD-2 parameters and identifying critical deviations based on
signal timing characteristics. A software package for system modeling and testing was created, allowing the verification
of its effectiveness based on both synthetic data obtained in the MATLAB/Simulink environment and simulated scenarios.
A comparative analysis of fault detection accuracy using the proposed FSM model versus traditional threshold methods
demonstrated an increase in diagnostic reliability. The testing results showed that fault detection accuracy increased to
92.2%, while the false-positive rate decreased to 4.1% compared to classical OBD-2 data analysis methods. The proposed
approach reduced processing delay to 250 milliseconds per diagnostic cycle, making it applicable for real-time fault
detection.
Keywords: finite state machine, automotive diagnostics, OBD-2, software architecture, data integration,
simulation

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