Proceedings of International Conference on Applied Innovation in IT  ·  2026/07/22  ·  Vol. 14  ·  Issue 4  ·  pp. 1179–1186
Application of Artificial Intelligence in Diagnosing the Stress-Strain State of Electric Locomotive Bogie Elements
Galina Khromova, Davran Radjibaev, Gulnora Kosimova, Jingyong Zhang, Assylbek Kassenov and Diyorbek Bekmirzaev
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Abstract
Locomotive bogie frames take a long-term structural beating and catching deformation early matters for safety. Here we build a mathematical model and algorithm for the durability of VL80s electric locomotive bogie components, grounded in instrumented, non-destructive wheelset testing, and then ask a more specific question: can a neural network do useful diagnostic work on top of that deterministic model? The network we settle on draws in several classes of measurement data tied to the frame's load-bearing capacity. Each input gets weighted against the others, the weighted signals are summed, and an activation function carries out the final transformation - this is what sets the limits of what the network can actually learn. Tested against a modernized bogie frame reinforced with steel plates, the approach predicts load-bearing capacity reliably and holds up across a range of related engineering calculations. Of the architectures we tried, a single-layer perceptron worked best for the graphical diagnostics side.
Keywords
Electric Traction Rolling Stock Electric Locomotive Bogie Frames Stress-Strain State Diagnostics Reliability Durability Reliability Assessment Methodology AI in the Form of Neural Network Modeling Algorithm Neural Networks with a Single-Layer Perceptron Architecture
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