Keywords
automated diagnostics
failure prediction
machine learning
diagnostics optimization
digital technologies in motor transport
condition monitoring system
How to Cite
Abstract
we reviewed modern approaches to monitoring the technical condition of motor vehicles (MV), focusing on risk-based methods. We identified the main problems related to time constraints for inspections, lack of data on previous inspections, and coordination issues between employees. We proposed methods for optimizing inspections, including the use of failure prediction algorithms and automated diagnostic systems. We conducted a comparative analysis of time costs and the efficiency of the proposed solutions. Our conclusions allow enterprises to minimize diagnostic costs, increase the safety of MV operations, and improve production performance.
References
Aven T. On Some Foundational Issues Concerning the Relationship between Risk and Resilience. Risk Analysis. 2022;42:2062–2074. DOI: 10.1111/risa.13848.
Hore U. W., Wakde D. G. Intelligent Predictive Maintenance for Industrial Internet of Things (IIoT) Using Machine Learning Approach. Intelligent Cyber Physical Systems and Internet of Things. Editors: Hemanth J., Pelusi D., Chen J. Cham: Springer; 2023. DOI: 10.1007/978-3-031-18497-0_65.
Лушников А. А. Управление ремонтами в транспортных подразделениях ОАО «Сургутнефтегаз». Вестник СурГУ. 2014;2:106-111. Режим доступа: https://cyberleninka.ru/article/n/upravlenie-remontami-v-transportnyh-podrazdeleniyah-oao-surgutneftegaz.
Комова Ю., Хожай В., Соколова Т. Риск-ориентированный подход в управлении качеством. Сборник научных трудов Уральского федерального университета. 2021;31:202–210.
Шилкина А. Т., Варакина О. Е. Тенденции развития риск-ориентированного подхода в контексте индустрии 4.0. Научно-технические ведомости СПбГПУ. Экономические науки. 2019;12(1):9–20.
Макаренко Е. Н., Булгаков С. А. Исследование риск-ориентированного подхода и возможностей его применения во внутреннем контроле. Учет и статистика. 2021;2:19–28.
Anderson J., Lewis S. Decision Support Systems for Technical Diagnostics in Aerospace Engineering. Aerospace Science and Technology. 2019;92:292–303. DOI: 10.1016/j.ast.2019.04.010.
Campbell N., Gray J. Evaluation of Technical Conditions Using Advanced Diagnostic Tools. Engineering Analysis with Boundary Elements. 2019;109:98–109. DOI: 10.1016/j.enganabound.2019.05.006.
Fernandez R., Lopez M. Risk-Based Optimization of Technical Systems for Improved Reliability. Reliability Engineering and System Safety. 2019;124:256–270. DOI: 10.1016/j.ress.2019.01.014.
Taylor M. J., Wilson L. Application of Machine Learning in Risk-Based Inspection Systems. Reliability Engineering and System Safety. 2018;105:467–480. DOI: 10.1016/j.ress.2018.02.011.
Thomas L., Richardson S. Machine Learning Techniques for Predictive Maintenance. Procedia Computer Science. 2019;151:709–717. DOI: 10.1016/j.procs.2019.04.095.
Turner D., Hall M. Use of Artificial Intelligence in Predictive Maintenance Systems. Applied Soft Computing. 2019;85:105790. DOI: 10.1016/j.asoc.2019.105790.