Intelligent diagnosics of vehicles
DOI:
https://doi.org/10.30977/VEIT.2022.22.0.5Keywords:
vehicle, diagnostic methods, malfunction, intelligent systemsAbstract
Problem. Diagnostics or troubleshooting is an integral part of the operation of automotive technology, and as automotive systems become more complex, the need for diagnostic skills increases, so diagnostic methods by the human senses should be considered an integral part of technical diagnostics at all stages of a vehicle life cycle. Methodology. Analytical methods are used to study the methods of diagnosing vehicles with the help of the intellectual abilities of the operator-diagnostician. Results. The paper shows that the intellectual abilities of the operator-diagnostician play an important role in diagnosing vehicles, the advantages and disadvantages of such diagnostics are presented. The list of basic knowledge necessary for the operator-diagnostician is described as well as the type of operational documentation which is necessary to improve the efficiency of intelligent diagnostics. Intelligent diagnostics of vehicles is divided into stages and shows the wide possibilities of diagnosing by the senses and knowledge of the diagnostician. It is shown that a highly qualified diagnostician can significantly reduce the complexity of diagnosis. With qualified training, experienced mechanics determine up to 70-90% of malfunctions and failures of vehicles and units using organoleptic methods and simple tests. Originality. The stages of intelligent diagnostics of vehicles are singled out and the wide possibilities of diagnosing by the human senses and knowledge of diagnostics at these stages are shown. Practical value. The results of this work are intended for wide use, for example, for drivers, maintenance services, developers of operational and technical documentation, developers involved in the improvement of technical diagnostic tools, machine learning, etc.
References
Asad, B., Vaimann, T., Rassõlkin, A., Kallaste, A., & Belahcen, A. (2018). Review of Electrical Machines Diagnostic Methods Applicability in the Perspective of Industry 4.0. The Scientific Journal of Riga Technical University-Electrical, Control and Communication Engineering, 14(2), 108–116.
Hnatov, A., Argun, S., Tarasov, K., Hnatova, A., Migal, V., & Patļins, A. (2019). Researching the Model of Electric Propulsion system for bus with the Matlab Simulink. 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 1–6. https://doi.org/10.1109 / RTUCON48111.2019.8982352
Hu, X., Zhang, K., Liu, K., Lin, X., Dey, S., & Onori, S. (2020). Advanced fault diagnosis for lithium-ion battery systems.
Altinisik, A., & Hugul, O. (2020). The seven-step failure diagnosis in automotive industry. Engineering Failure Analysis, 116, 104702. https://doi.org/10.1016/j.engfailanal.2020.104702
Bogajevskiy, A., Arhun, S., Hnatov, A., Dvadnenko, V., Kunicina, N., & Patlins, A. (2019). Selection of Methods for Modernizing the Regulator of the Rotation Frequency of Locomotive Diesels. 1–6.
Moniz, A. B., & Krings, B.-J. (2016). Robots Working with Humans or Humans Working with Robots? Searching for Social Dimensions in New Human-Robot Interaction in Industry. Societies, 6(3), Article 3. https://doi.org/10.3390/soc6030023
Oborski, P. (2004). Man-machine interactions in advanced manufacturing systems. The International Journal of Advanced Manufacturing Technology, 23(3), 227–232. https://doi.org/10.1007/s00170-003-1574-5
Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018). Machine learning for molecular and materials science. Nature, 559(7715), 547–555.
Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—Addressing ethical challenges. The New England Journal of Medicine, 378(11), 981.
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., & Nandi, A. K. (2020). Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, 106587.
Lejda, K., & Zielińska, E. (2014). General criteria for technical facilities with respect to the correct motor vehicle and combustion engine diagnostics and in the context of environmental protection. Journal of Polish CIMAC, 9(2), 137--144.
Tsybunov, E., Shubenkova, K., Buyvol, P., & Mukhametdinov, E. (2017). Interactive (intelligent) integrated system for the road vehicles’ diagnostics. First International Conference on Intelligent Transport Systems, 195–204.
Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020). Integrating Machine Learning with Human Knowledge. IScience, 23(11), 101656. https://doi.org/10.1016/j.isci.2020.101656
Oliff, H., Liu, Y., Kumar, M., & Williams, M. (2018). A Framework of Integrating Knowledge of Human Factors to Facilitate HMI and Collaboration in Intelligent Manufacturing. Procedia CIRP, 72, 135–140. https://doi.org/10.1016/j.procir.2018.03.047
Arhun, S., Migal, V., Hnatov, A., Hnatova, H., & Ulyanets, O. (2020). System Approach to the Evaluation of the Traction Electric Motor Quality. EAI Endorsed Transactions on Energy Web, 7(26), 1–9. https://doi.org/10.4108/EAI.13-7-2018.162733
Migal, V., Lebedev, A., Shuliak, M., Kalinin, E., Arhun, S., & Korohodskyi, V. (2021). Reducing the vibration of bearing units of electric vehicle asynchronous traction motors. Journal of Vibration and Control, 27(9–10), 1123–1131. https://doi.org/10.1177/1077546320937634
Arhun, S., Hnatov, A., Migal, V., & Ponikarovska, S. (2020). Determining the quality of electric motors by vibro-diagnostic characteristics. EAI Endorsed Transactions on Energy Web, 7(29(e6)), 1–8. https://doi.org/10.4108/eai.13-7-2018.164101
Mygal, V., Arhun, S., Hnatov, A., Bagach, R., & Kunicina, N. (2022, May 9). Methods for Diagnosing Vehicles by an Operator-Diagnostician. 2022 IEEE 63nd International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), Riga, Latvia: Riga Technical University.
Migal, V. D., & Migal, V. P. (2014). Methods for technical diagnostics of cars. ID «Forum», INFRA-M.
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Copyright (c) 2022 Василь Мигаль, Щасяна Аргун, Андрій Гнатов, Ганна Гнатова, Павло Сохін
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