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The dynamic characteristics of the hydraulic damper are time-varying in the complex working environment. To reveal the internal influence mechanism of the boundary conditions on the dynamic performance of the hydraulic damper and take it into account in the multi-body dynamics calculation, the laboratory test of the hydraulic damper is carried out firstly, and it is confirmed that the hydraulic damper has significant frequency-dependent and amplitude-dependent and temperature-dependent characteristics. Then, combining the physical parameter model with the neural network model, an accurate hybrid neural network model of the hydraulic damper is proposed. The physical parameter model considers the damper structure, including orifice, damping valve, rubber joint and the relationship between temperature and viscosity of hydraulic oil. The neural network model describes the personality characteristics of the hydraulic damper, such as oil leakage, the internal friction force and the percentage of entrapped air in oil. Finally, the responses and the dynamic parameters of the hybrid neural network model are calculated and compared with the experimental results by considering various exciting amplitudes and frequencies. The results show that the proposed model can fully simulate the dynamic performance of the hydraulic damper under various operating conditions. [ABSTRACT FROM AUTHOR]

To fully consider the influences of the exciting frequency (0–20 Hz) and the environmental temperature (−50–20°C) on the dynamic characteristics of the rubber bushing in the railway vehicle, a hybrid neural network model is applied to develop the detailed model of the rubber bushing. The nonlinearities of the rubber bushing existing in the elastic element, the friction element and the viscous element are involved in the model to reflect the frequency- and amplitude-dependent characteristics of the rubber bushing. The experimental data are collected and exploited to find out the relationship between the input and output with the help of the neural network model. Furthermore, the influences of the environmental temperature on the dynamic parameters of the rubber bushing are considered. By testing the dynamic performance of the rubber bushing under the excitation with different frequencies, amplitudes and temperatures, the hybrid neural network model is trained and its critical parameters are identified. In this way, the trained hybrid neural network model for the rubber bushing can accurately reflect the dynamic performance in the presence of various excitation and environment temperatures. [ABSTRACT FROM AUTHOR]