Rail transportation is regarded as a reliable, quick, and secure mode of transportation. The wheel-rail contact interaction is crucial to the railway operation since it is responsible for supporting, traction, braking, and steering of railway vehicles. Improper wheel-rail interactions may produce or exacerbate wheel-rail interface issues such as rolling contact fatigue (RCF) and wear, which can threaten the vehicle’s running safety and stability. A review of the evolution and recent literature on wheel-rail contact mechanics and tribology is presented here. Topics covered include the basics of wheel-rail contact problem and methodologies for modeling both the normal contact (Hertzian and non-Hertzian) and tangential contact (Kalker’s theories including CONTACT and FASTSIM algorithms, Polach’s theory, USETAB program, etc.). The paper also reviewed various effects of contaminants and environmental conditions (water, leaves, sand, temperature, humidity, etc.) in wheel-rail contact. Various wheel-rail empirical adhesion models like the Water-induced low adhesion creep force model (WILAC) model and adhesion models based on elastohydrodynamic lubrication (EHL) theory (Greenwood-Tripp [GT] and Greenwood-Williamson [GW] models) are also reviewed. Lastly, the paper discusses the need and challenges for developing and integrating the wheel-rail non-Hertz contact model and adhesion model, as well as open areas for further research.

Unsupervised domain adaptation-based transfer learning (TL) has been widely used in rolling bearing fault diagnosis to overcome the problem of limited and non-identically distributed labeled data. Discrepancy-based alignment is a popular domain adaptation method in TL. However, due to the inability to completely eliminate domain drift, the classifier learned from the source domain may easily misclassify some target domain samples that are scattered near the decision edge. In this work, a multi-order moment matching-based domain adaptation is proposed to address the issue. Low- and high-order moment matching is simultaneously applied to describe the complex non-Gaussian distributions in more detail and realize coarse- and fine-grained hybrid domain alignment. Furthermore, a discriminative clustering approach is employed to extract domain-invariant features of inter-class discrimination and intra-class compactness, which effectively reduces the negative transfer caused by hard-aligned target samples. The application of the proposed model to the experimental dataset demonstrates that the model can significantly improve the diagnosis accuracy of rolling bearing faults in cross-working conditions. This study can be of assistance to engineers in promptly identifying and addressing rolling bearing faults, ultimately enhancing the reliability and safety of equipment.

The elastohydrodynamic lubrication (EHL) oil film between contact interfaces acts as a spring or damper to reduce wear and vibration for frictional pairs. To analyze the dynamic behaviors of friction pairs in mechanical systems both effectively and accurately, the stiffness and damping parameters under EHL contact states are essential. The presented work develops a numerical model to investigate the EHL stiffness and damping characteristics based on the transient EHL system and elastic contact theory of line contact, in which the stiffness force is separated according to the relationship with approach distance of the contact body established in the steady process, and then the damping can be obtained. The results show that the stiffness force plays an increasingly important role over the applied load conditions while the damping effects is gradually weakened. EHL stiffness is obviously smaller than dry contact stiffness, but the discrepancy is decreasing with the increasing load. Moreover, the higher entrainment velocity, lubricant viscosity and larger curvature radii leads to smaller stiffness and damping. The elastic modulus generates little effect on dynamic characteristics when the load is light while dominates the maximum level of the contact stiffness.