Mechanical engineering and transport

Аnalytical Approaches to the Development of Intelligent Quality Control Systems for the Post-Repair Condition of Automobiles

Authors

O. V. Gorbenko
Poltava State Agrarian University ROR
T. H. Lapenko
Poltava State Agrarian University ROR
DOI https://doi.org/10.31649/1997-9266-2025-183-6-158-165

Keywords

post-repair condition control service infrastructure resource prediction standardization economic effect

Abstract

The article examines the issues of post-repair condition control of automobiles, which are traditionally carried out through visual inspection, measurement of individual parameters, bench testing, and short-term operational checks. It is shown that such methods are fragmented, depend on the subjective factor, and do not allow prediction of the residual service life of components. This highlights the need to implement intelligent diagnostic systems capable of providing comprehensive monitoring based on sensor networks, digital twins, and machine learning algorithms.

Modern approaches to the integration of sensors, monitoring systems, and artificial intelligence into the process of post-repair assessment of vehicles are analyzed. The advantages of applying ensemble algorithms (Random Forest, XGBoost) and neural networks (RNN, LSTM) are substantiated, as they provide the highest accuracy in detecting hidden defects and predicting component life. A multi-level architecture of the intelligent control system is presented, which includes hardware, software and communication, analytical, service, and regulatory-security layers, capable of integration with existing service platforms.

Special attention is paid to the economic assessment of implementing such systems using the example of automatic transmission repair. Modeling results confirmed the reduction in the number of repeated repairs, a decrease in labor costs, and additional income from premium services, which ensures an annual effect of over $40,000 and a cumulative benefit exceeding $200,000 over a five-year period.

It is noted that the effective implementation of intelligent control systems requires the development of unified methodologies for post-repair assessment, standardization of diagnostic data exchange protocols, and mandatory certification of the respective hardware and software. This creates the basis for the formation of a sustainable service infrastructure, reduction of operational costs, and improvement of road transport safety.

117 65

How to Cite

[1]
“Аnalytical Approaches to the Development of Intelligent Quality Control Systems for the Post-Repair Condition of Automobiles”, Вісник ВПІ, no. 6, pp. 158–165, Dec. 2025, doi: 10.31649/1997-9266-2025-183-6-158-165.

Author Biographies

O. V. Gorbenko, Poltava State Agrarian University

Cand. Sc. (Eng.), Associate Professor, Associate Professor of the Chair of Agroengineering and Automotive Transport

T. H. Lapenko, Poltava State Agrarian University

Cand. Sc. (Eng.), Associate Professor, Professor of the Chair of Agroengineering and Automotive Transport

References

J. E. See, Visual Inspection: A Review of the Literature, Oak Ridge, TN, USA: U.S. Department of Energy, 2012. [Electronic resource]. Available: https://www.osti.gov/servlets/purl/1055636 .
S. Liu, “A review of applications of visual inspection technology based …,” Trans. Inst. Meas. Eval. vol. 1, no. 3, pp. 185-197, 2019.
H. Liu, B. Zhang, V. Wu, X. Yang, and L. Wang, “Review of Digital Twin in the Automotive Industry on Products, Processes and Systems,” International Journal of Automotive Manufacturing and Materials, vol. 4, no. 1, Mar. 2025, Art. 6. https://doi.org/10.53941/ijamm.2025.100006 .
S. Werbińska-Wojciechowska, “Digital Twin Approach for Operation and Maintenance of Transportation Systems,” Sensors, vol. 24, no. 18, Art. 6069, 2024. https://doi.org/10.3390/s24186069 .
L. M. Prikler, and F. Wotawa, “A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation,” arXiv Preprint, Feb. 2024. [Electronic resource]. Available: https://arxiv.org/abs/2402.01686 .
R. Jafari, and Y. C. Byun, “Prediction of the Battery State Using the Digital Twin Framework Based on the Battery Management System,” IEEE Access, vol. 10, pp. 124685-124696, 2022. https://doi.org/10.1109/ACCESS.2022.3225093 .
N. D. K. M. Eaty, and P. Bagade, “Digital Twin for Electric Vehicle Battery Management with Incremental Learning,” Expert Systems with Applications, vol. 229, Art. 120 444, 2023 https://doi.org/10.1016/j.eswa.2023.120444 .
S. Ibrahim, “Overview of digital twin platforms for EV applications,” Sensors, vol. 23, no. 3, Art. 1414, 2023. https://doi.org/10.3390/s23031414 .
M. Yue, K. Benaggoune, J. Meng, and D. Diallo, “Implementation of an Early Stage Fuel Cell Degradation Prediction Digital Twin Based on Transfer Learning,” IEEE Trans. Transport Electrification, vol. 9, pp. 3308-3318, 2023. https://doi.org/10.1109/TTE.2022.3229718 .
M. Ezhilarasu, Z. Skaf, and I. K. Jennions, “Understanding the Role of a Digital Twin in Integrated Vehicle Health Management (IVHM),” у Proc. IEEE Int. Conf. Systems, Man and Cybernetics (SMC), Bari, Italy, Oct. 2019, pp. 1484-1491.
C. M. Ezhilarasu, Z. Skaf, and I. K. Jennions, “Understanding the Role of a Digital Twin in Integrated Vehicle Health Management (IVHM),” Proc. IEEE Int. Conf. Systems, Man and Cybernetics (SMC), Bari, Italy, Oct. 2019, pp. 1484-1491.
“Artificial Intelligence for Quality Defects in the Automotive Industry: A Systemic Review,” Sensors, Feb. 2025. [Electronic resource]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11902312 .