(untitled) (Science journal «Technical service of agriculture, forestry and transport systems», 2025)
The technical state prediction of automotive transmissions plays a pivotal role in ensuring the reliability, safety, and cost-effectiveness of modern vehicles. Automotivetransmissions operate under variable and often harsh conditions, which makes early detection of defects and accurate life prediction critical challenges in vehicle maintenance. This study presents a comprehensive methodology for diagnosing and forecasting the technical state of automotive transmissions by leveraging advanced signal analysis, state-of-the-art artificial intelligence techniques, and mathematical modeling of transient processes. The proposed approach begins with the acquisition of high-precision multi-source data, including vibration, acoustic, and thermal signals collected under real-world operational conditions. Signal preprocessing methods, such as noise reduction and feature extraction using Fourier and Wavelet Transform techniques, are applied to isolate diagnostically significant features. Advanced machine learning algorithms-including neural networks, support vector machines, and ensemble methods such as Random Forest and Gradient Boosting - are employed to classify defect types and predict the remaining useful life of components. Complementary mathematical modeling techniques, including finite element analysis and numerical solutions for nonlinear systems, are used to simulate the dynamics of transmission components under variable loads. Experimental validation demonstrates the methodology’s effectiveness in diagnosing early-stage defects and providing accurate life predictions, significantly reducing unscheduled downtimes. The integration of this methodology into automated monitoring systems improves diagnostic precision and optimizes maintenance schedules, enhancing overall vehicle reliability and safety. Future research will focus on extending this methodology to accommodate emerging vehicle technologies, such as electric and hybrid powertrains, while addressing the challenges of explainability in AI-driven diagnostics.