Wenjing Sun

Abnormal vibration signals of tramcar are mostly nonstationary and nonlinear signals. This study applied the Hilbert–Huang transform (HHT) to analyze abnormal vibration of the tramcar, aiming to overcome the limitations of some traditional time-frequency analysis methods, such as Fourier transform, in dealing with nonstationary and nonlinear signals. Additionally, to address mode aliasing produced during empirical mode decomposition (EMD) used in classical HHT, this study proposed to first use complete EMD with adaptive noise for the decomposition of original vibration data, then eliminate the trend-term components with the calculated correlation coefficients, and finally perform denoising on high-frequency noisy components using the wavelet threshold method. After weighted reconstruction using denoised high-frequency components and low-frequency information components, data processing was finally optimized via HHT. Taking a tramcar as an example, the Hilbert spectra of the vertical acceleration of axle box were plotted via HHT, and the time-instantaneous, frequency-instantaneous energy 3D relations were obtained for the location of impact points. Further, the vibration characteristics were analyzed and quality indexes were calculated in combination with the marginal spectra so as to judge the reasons for abnormal vibration and failure modes of the tramcar. The results revealed that the proposed method was feasible and effective in vibration feature extraction and vibration impact analysis for tramcars.

The accuracy of the physical parameters and excitation parameters of the under-chassis equipment has a significant impact on investigations of the coupled vibration of the vehicle body and the under-chassis equipment. In this study, an equipment vibration isolator test bench is used to develop a physical parameter identification method based on the free vibration acceleration response of the equipment and an excitation parameter identification method based on the forced vibration acceleration response. The equipment mass parameter and the center of the gravity position parameter can be obtained first through a static load test, and then, the inertia parameters can be obtained through a dynamic test. Identification of the excitation parameters of the equipment is based on the physical parameters and the acceleration response. The accuracy of the physical parameters directly affects the excitation parameter identification results. The larger the frequency ratio, the smaller the identification error will be, and the larger the damping ratio, the larger the identification error will be. The identification test results of single-frequency excitation and multifrequency excitation show that the proposed excitation parameter identification method has high accuracy.

In some metro vehicles, large numbers of failures of coil springs in the primary suspension have occurred due to fatigue fracture. The cause of these failures is investigated by studying the vehicle/track interaction, the modal response of the coil springs and the stresses occurring within them in working conditions. A finite element model of the primary suspension is used to determine the dynamic stiffness matrix, which is then included in a multi-body model of the vehicle and coupled to the track. The springs exhibit strong internal resonances at around 50–60 Hz, at which very large stresses occur in both springs. This frequency range coincides with the P2 resonance frequency (wheelset mass bouncing on the track stiffness) for the standard slab track system used on this metro system. For other track systems, the P2 resonance occurs at a different frequency and the stresses are lower. These results are confirmed with field test data. From the stresses the weakest position in the inner spring is identified, which corresponds to the position of common breakages found in field observations. Some guidelines are proposed for reducing the vibration and stress, so that the fatigue fracture incidents can be reduced.

Mechanical properties of floor prototypes made for high‐speed trains from composite multilayer floor structures of different materials and thicknesses were tested. Based on the test results, the equivalent mechanical parameters of different layers of the panels, core materials, etc., were calculated, and the multilayer mixed finite element model of the floor was built and verified. The multilayer mixed floor model was introduced into the car body of a high‐speed train, and the test signals of the high‐speed train were used as inputs to calculate the vibration response of composite floor structure. Vibration spectra of the high‐speed train composite floor made of different materials and structures were compared and analyzed. The results show that the calculation of the floor vibration response of the vehicle car body within the framework of the equivalent model of the multilayer structure based on the mechanical performance test is an effective method to evaluate vibration characteristics. The vibration isolation performance of the stainless steel panel floor is better than that of the aluminum alloy panel, but its large mass is not favorable in lightweight body design. The vibration energy of birch core material floor is significantly smaller than the alder core material of the same size. The vibration isolation performance of the floor enhanced with the increase in the thickness of the outer metal panel, but when the outer metal panel thickness exceeds 0.8 mm, the influence becomes small. Therefore, the stainless steel‐birch core composite floor with a large panel thickness has the best vibration isolation performance and can be used for the floor above the bogie and the suspended excitation source. Then, an optimal analysis considering mass and vibration characteristics was carried out, and the results show that there exists an optimal solution for the high‐speed train. Regarding the overall design basis of the vehicle, the aluminum alloy panel lightweight structure can be used in combination with other general parts.