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- W3036640544 abstract "• Efficient experimental measurement of lateral wheel/rail contact forces using ANNs. • Results are compared to the use of the harmonic cancellation method. • The proposed ANN method requires less sensorisation to measure contact forces. • Wheel/rail contact forces are validated using a scaled dynamometric wheelset. • The works opens the door to apply ANNs to other classical railway dynamics problems. This paper presents a method for the experimental measurement of the lateral wheel-rail contact force based on Artificial Neural Networks (ANN). It is intended to demonstrate how an Artificial Intelligence (AI) method proves to be a valid alternative to other approaches based on sophisticated mathematical models when it is applied to the wheel-rail contact force measurement problem. This manuscript addresses the problem from a computational and experimental approach. The artificial intelligence algorithm has been experimentally tested in a real scenario using a 1:10 instrumented scaled railway vehicle equipped with a dynamometric wheelset running on a 5-inch-wide track. The obtained results show that the ANN approach is an easy and computationally efficient method to measure the applied lateral force on the instrumented wheel that requires the use of fewer sensors." @default.
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- W3036640544 date "2020-11-01" @default.
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- W3036640544 title "Artificial neural networks applied to the measurement of lateral wheel-rail contact force: A comparison with a harmonic cancellation method" @default.
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- W3036640544 doi "https://doi.org/10.1016/j.mechmachtheory.2020.103968" @default.
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