Search Results (91)

The paper is focused on the problem of increasing the reliability of determining the critical speed of railway vehicles during measurements in bench tests, due to the difference in the geometry of the contact interaction in the “wheel-roller” and “wheel-rail” systems. It is shown that existing approaches in dynamic stability analysis and the processing of measurement signals do not consider the systematic influence of equivalent conicity on the measurement results, which leads to a shift in the assessment of the threshold for loss of stability even with the high accuracy of the measuring equipment. An information-measuring approach is proposed, which is based on an analytical description of the relationship between the equivalent conicity of the wheel/rail contact and the critical speed. A generalized error model is developed integrating geometric, metrological and random components. A method for correcting the results of bench tests based on a coefficient considering the ratio of equivalent conicities is also proposed. It was found through the results of the research that neglecting the geometric mismatch of the contact interaction leads to an error in determining the critical speed at the level of 8 to 15%, while the application of the proposed approach reduces it to less than 1 to 2%. Additionally, frequency analysis of the oscillatory process was used. It allowed us to identify the critical mode of the frequency range of about 8 Hz and to establish its connection with the critical speed of movement. It was experimentally confirmed that the corrected results correspond with the calculated values within 1%. The obtained results provide an increase in the metrological consistency of bench and operational studies, expand the possibilities of interpreting test results and can be used in the development of methods for diagnostics, certification and prediction of the limit modes of operation of railway vehicles. The proposed approach provides increased reliability in determining the critical speed during bench tests and it can be used for analytical compensation of a systematic error without upgrading the test equipment. Full article

The paper describes the design and calibration of an innovative scaled twin-disc test rig, which enables the investigation of both the wheel–rail and wheel–shoe contact interactions. The design of the main bench components, including the load application and tread braking systems, was performed to comply with specific scaling rules, allowing for an easy correlation of data obtained on the scaled bench with the real-world scenario. The preliminary calibration of the load application system and tread braking unit proves excellent repeatability and linearity, thus confirming the robustness and validity of the bench design. Full article

The wet-rail phenomenon can cause low adhesion, which negatively affects railway operation. It is believed to occur when small amounts of water mix with solid particles on wheel and rail surfaces, e.g., wear debris or iron oxides, forming a dense suspension in the wheel–rail contact, leading to sharp adhesion drops. Mini Traction Machine (MTM) tests using water-based suspensions with different particles also show adhesion drops during water evaporation, which can be linked to the wet-rail phenomenon. While the physical mechanisms underlying the adhesion drop are unclear, it is hypothesised that rapid loading raises fluid pressure in the suspension, separating wheel and rail surfaces, reducing force transfer through particle contact, thereby reducing the suspension’s shear strength. For verification, a coupled Discrete Element Method and fluid dynamics model is used to simulate a simplified MTM setting and steps towards full scale wheel–rail contact. During simulation of rapid loading, fluid pressure rises but remains negligible compared to applied contact stresses in all considered cases. Thus, it is unlikely that hydrodynamic pressure build-up within the suspension contributes significantly to the low adhesion observed. Future research should investigate additional mechanisms, such as reduced shear strength of deformed or crushed wet particles under high normal loading conditions. Full article

The occurrence of short-wave corrugation with wavelengths of 32–44 mm on curved sections of urban express railway lines is particularly pronounced, yet the underlying initiation mechanisms have remained insufficiently understood. Furthermore, conventional mitigation strategies—including the installation of rail dampers and passive grinding—entail substantial maintenance expenditures, thereby hindering their large-scale application. To elucidate the initiation mechanisms of rail corrugation and to formulate effective control measures, the characteristic corrugation parameters under various track structure configurations across an entire alignment were first measured and systematically analyzed. Dynamic interaction models between vehicles and three distinct track typologies were subsequently developed, together with a comprehensive analytical framework for corrugation evolution. The wheel–rail dynamic response characteristics and corrugation growth rates corresponding to each track type were examined, and the wheel–rail coupled vibration modes that exacerbate corrugation propagation in urban express lines were identified. The instantaneous wear behavior of the rail under differing creep regimes was also investigated, leading to the proposal of a novel self-mitigating approach for rail corrugation. The results demonstrate that the excitation frequency of rail corrugation is predominantly confined to the 600–700 Hz range, exhibiting a fixed-frequency characteristic that remains invariant with respect to curve radius, track structure type, and operational speed. An interesting finding is that, although the intrinsic vibration properties of different track structures diverge significantly, the third-order bending resonance of the rail segment situated between bogie wheels is largely unaffected by track-borne vibrations and manifests as a localized wheel–rail resonance within the vehicle–track coupled system. This particular resonance markedly accelerates corrugation development and is identified as the critical governing factor for corrugation initiation in urban express lines, regardless of the underlying track configuration. Furthermore, rail instantaneous wear displays a substantial phase shift under varying creep conditions, with the wear profiles under creep saturation (full sliding) and low creep (rolling–sliding) exhibiting a distinct anti-phase relationship. This insight underpins a novel self-wear suppression strategy: by intentionally mixing rolling–sliding and full-sliding operational regimes, destructive interference between the out-of-phase wear contributions is achieved, resulting in a considerably attenuated corrugation growth rate compared with exclusive rolling–sliding operation. This methodology thus offers a promising and fundamentally new alternative for the long-term management of rail corrugation through intrinsic wheel–rail interaction. Full article

Artificial-intelligence-driven wayside monitoring has become a promising solution for early identification of railway wheel flats, enabling safer operations and more efficient maintenance planning. This study introduces a comparative investigation of supervised and unsupervised machine learning strategies for wheel flat identification, with particular emphasis on real-time applicability and sensor cost reduction. Support Vector Machines (SVMs) and k-means clustering are evaluated as representative supervised and unsupervised approaches using vibration data obtained from numerically simulated train–track interactions under realistic operating conditions, including train speeds of 120 km/h and 200 km/h and multiple wheel flat severities. A key contribution of this work is the proposal of a simplified supervised classification framework that directly exploits Auto-Regressive features extracted from rail-mounted accelerometers, eliminating the need for feature normalization and multi-sensor data fusion. This simplification significantly reduces computational effort, making the approach suitable for real-time deployment in operational railway environments. In parallel, a systematic sensitivity analysis is conducted to assess the influence of sensor placement and to identify the minimum sensor configuration required to achieve reliable damage classification. The outputs from the current study show that an SVM emerges with more accurate defect classification than the k-means clustering, allowing a wayside system with fewer sensors. Full article

Ensuring the structural integrity of tower cranes is paramount for construction safety, yet jib lower chords—serving as trolley tracks—often undergo coupled wear–fatigue degradation that is rarely quantified in conventional service-life assessments. This study proposes a quantitative, maintenance-focused framework for integrity evaluation and life prediction of in-service tower cranes, validated through a decommissioned unit with 26 years of service in high-rise building construction. Through the integration of on-site construction operational statistics, ANSYS (Version 2022 R1, ANSYS, Inc., Canonsburg, PA, USA)—driven stress simulations, and rainflow counting, a multi-condition load spectrum was developed to quantify cumulative damage. Field measurements pinpointed Segment b03 as the critical damage zone, showcasing a maximum wear depth of 2.3 mm and roughly 30% thickness loss in the 20–30 m range, driven by stress concentration and high-frequency trolley movements during material handling. Theoretical fatigue life estimates of 42.1 years were revised to 24.1 years by incorporating wear geometry evolution and other degradation factors, resulting in a prediction error of approximately 7–8% relative to the actual service life. The proposed approach effectively bridges the gap between mechanical-based calculations and construction engineering practice, providing robust support for inspection scheduling, maintenance prioritization, and lifecycle management of aging tower cranes. Full article

The dynamic behavior of a double-beam configuration subjected to a harmonic moving load was studied in this paper. The model was built to represent the wheel–track system that was composed of two infinite Timoshenko beams joined by uniformly spaced sleepers and supported by a continuous viscoelastic foundation. The response of the coupled beams to a moving harmonic excitation was first derived, after which the wheel–rail interaction was incorporated through a generalized Fourier series formulation. The associated Fourier coefficients were obtained from a finite system of algebraic equations imposed by the wheel–track contact conditions. The numerical simulation was carried out to compare the predictions of the Timoshenko and Euler–Bernoulli beam assumptions and to explore the influence of load speed and excitation frequency on the dynamic characteristics of the double-beam system. Comparative analysis reveals that Timoshenko beam theory predicts larger vertical displacements for rail, slab, and sleeper near the model’s cut-off frequencies (20 Hz and 30 Hz) than Euler–Bernoulli theory, with higher load velocities reducing the first cut-off frequency and amplifying peak amplitudes. The dynamic response exhibits two critical velocities at sub-cut-off frequencies, where rail displacements increase with load velocity, whereas this trend reverses when the load frequency meets or exceeds the cut-off frequencies, and no distinct peaks occur at 25 Hz and 40 Hz. The research findings are of great significance for the vibration propagation and vibration disaster prevention for shield tunnels during the train operation. Full article

Irregular wheel wear can significantly degrade wheel–rail interaction performance and, in severe cases, compromise the safety of high-speed trains. Accurate and timely monitoring of wheel wear is crucial for maintaining operational reliability. Existing monitoring methods often rely on high-end sensors or are sensitive to environmental disturbances, limiting their practical deployment. This study proposes a novel method for monitoring irregular wheel wear by analyzing the stator current spectrum of traction motors. Firstly, an electromechanical coupled model is developed by integrating the electric drive system with the vehicle–track dynamic model to capture the propagation of wear-induced excitation. The effect of polygonal wear on the stator current is investigated, revealing the presence of harmonic components coupled with the wear excitation frequency. To extract these features, a comb filter based on Variational Mode Decomposition (VMD) is introduced. The method effectively isolates wheel wear-related harmonics from existing electrical harmonics in the stator current signal. Simulation results demonstrate that the proposed approach can accurately detect harmonic features caused by polygonal wear, validating its applicability. This method provides a feasible and non-intrusive solution for wheel wear monitoring, offering theoretical support for condition-based maintenance of high-speed rail systems. Full article

During train operation, the adhesion characteristics between the wheels and rails, which are influenced by driving environments and operating conditions, result in a traction force lower than the motor’s nominal output. Traditional control strategies often overlook the nonlinear relationship between wheel–rail adhesion limits and traction motor output, which can lead to wheel slippage, accelerated wear, and excessive energy consumption. This paper establishes an energy-efficient train control model considering wheel–rail adhesion characteristics. Based on convex optimization methods, the model jointly optimizes the train’s speed trajectory and motor control strategy. Before optimization, nonlinear constraints are simplified through function approximation and tightened McCormick envelope relaxation, significantly reducing the computational complexity of the model. Numerical experiments demonstrate that the proposed driving strategy can adjust the train’s speed in response to poor rail conditions, ensuring adherence to adhesion safety limits. Simulations based on real-world high-speed rail line data in China show that, compared to the traditional EETC model with anti-skid control measures, the proposed model achieves a safer driving strategy. Additionally, in the context of speed trajectory tracking control, it reduces energy consumption by 19.49% compared to the traditional EETC model with anti-skid control measures. Furthermore, the model demonstrates high computational efficiency, indicating its potential for integration into a real-time driving strategy optimization framework. Full article

This paper reviews, analyses, and suggests practical mitigation techniques at source for reducing vibration-induced annoyance to occupants in building structures that are built on top of significant railway infrastructure. The dynamic characteristics of vibration caused by wheel-rail interaction at metro train depots are different from those on main-lines and conventional studies. Ground-borne vibration in a building directly above a double-deck railway depot was investigated, focusing on vibration attenuation through rail track design, which is more effective and economic compared to treatments at receivers or along prorogation paths. A 2.5-Dimensional finite element model was established to simulate vibration transmission using different combinations of track-forms. Source contribution under different train running conditions has been evaluated by computing vibration levels along the main transmission path. Vibration levels at representative positions in the building rooms have been predicted using the numerical model and have been compared against site measurements at the corresponding locations after the completion of the construction of the depot and buildings. It was found that the 2.5D FE model enables a reasonable prediction of ground-borne vibration from the metro depot, and that by appropriate design of the track-form, a good level of vibration attenuation can be achieved in an economical way. Full article

Turnouts represent a critical element of railway infrastructure, and are subjected to some of the highest mechanical stresses due to the discontinuity of the track geometry. Failures in this area generate high maintenance costs and may compromise traffic safety. This study investigates the effect of wheel profile geometry on the wheel–turnout interaction in the presence of multi-point contact. Two standard wheel profiles, S78 and S1002, are compared using numerical simulations based on Kalker’s three-dimensional rolling contact theory, implemented in the CONTACT program. The methodology includes parametric analysis of the contact stresses, adhesion/slip distribution, and frictional power density for typical operational conditions. It was observed that the choice of wheel profile significantly influences the shape and load distribution of contact patches, with direct implications for wear mechanisms and guidance safety. These findings provide valuable insight for optimizing wheel–rail interface design and for reducing turnout maintenance costs. Full article

Short-pitch rail corrugation is commonly found at curves or resilient track structures of the metro system, causing fatigue failure of key components of the train–track system. Currently, rail corrugation is detected via routine inspections during the possession period, with the compromise between inspection efficiency and data accuracy. A newly proposed indirect diagnosis method for rail corrugation has been proposed. Rail corrugation dynamic characteristics and location can be quantitatively identified by measuring train vehicle interior noise and vibration response of a running train under the normal operation conditions, without requiring track access, together with transfer functions—including receptance and accelerance of the wheel–rail system. This indirect method has been applied to and tested on a rail track section with severe corrugation at curves. Results from the indirect diagnosis method are then compared against direct rail roughness measurement using a standard Corrugation Analysis Trolley. Good agreements of peak magnitudes and corresponding frequency bands have been achieved. The indirect method has been successfully validated and can be used to assist track maintenances. Full article

With the continuous increase in high-speed train operation speeds, lightweight bogie design has become a key means to enhance dynamic performance, which also increases the risk of structural fatigue. High-frequency wheel–rail excitations are transmitted to the bogie frame and couple with its higher-order modes at around 200 Hz, inducing local high-frequency resonance. This coupling markedly increases the stress amplitude within the affected frequency range and accelerates vibration-induced fatigue damage. This study investigates the vibration fatigue characteristics of a bogie frame with an inner axle box under high-speed operation and wheel polygon wear conditions. Using a high-speed wheel–rail interaction test rig, dynamic stresses and the vibration acceleration of the bogie frame are measured under different speeds and polygon orders. Based on modal analysis and vibration fatigue methods, a high-frequency vibration fatigue assessment method for the bogie is developed. Wheel polygon significantly amplifies mid-to-high-frequency vibration energy, and for the bogie frame with an inner axle box, pronounced modal coupling is observed at around 200 Hz. In particular, under the 11th-order polygon condition, the equivalent stress at critical locations such as the traction motor seat weld seam exceeds the fatigue limit, while the effect of the 20th-order polygon is relatively mitigated. The proposed vibration fatigue assessment method provides a theoretical basis for the safe design and operational maintenance of high-speed trains with bogie frames with inner axle boxes. Full article

Railway braking efficiency hinges on the thermomechanical conditions at the wheel-rail interface. Frictional heating during operation causes significant temperature fluctuations, directly impacting braking performance in rail vehicles. Evaluating these effects is important for developing infrastructure and components adapted to environmental conditions. Several studies have explored the influence of temperature on components such as the brake disc or the wheel; little attention has been paid to the thermal conditions of the rail itself. This paper examines the effect of rail temperature on the braking behavior and energy consumption of a railway vehicle. Using a 1:20 railway track, rail segments were subjected to four temperatures (28.5 °C, 40.0 °C, 49.9 °C, 71.0 °C) by heating with Nichrome wire, and tests were performed at three speeds (0.75, 1.00, and 1.30 m/s). The results show that higher rail temperatures improve wheel-rail adhesion up to an optimum point (40.0 °C), beyond which performance deteriorates. In contrast, tests at 71.0 °C showed reduced braking efficiency, despite lower electrical current peaks, indicating a non-linear thermal response. Full article

The assessment of wheel–rail force states is a key technical issue in the safety monitoring of high-speed railway turnouts. Due to the complex geometry and severe load fluctuations of turnouts, wheel–rail interactions exhibit strong nonlinearity, asymmetry, and multidimensional coupling characteristics. Traditional methods suffer from limitations such as reliance on labeled samples and poor real-time performance. This study proposes an intelligent evaluation method that integrates multivariate statistical analysis with unsupervised clustering, and establishes a multidimensional analytical framework incorporating data preprocessing, time-domain analysis, safety index evaluation, frequency-domain feature extraction, and cluster-based recognition. Using a turnout section of the Beijing–Tianjin Intercity Railway as a case study, four fundamental wheel–rail force components were selected as feature variables to reveal their dynamic patterns. The DBSCAN density-based clustering algorithm was employed to achieve unsupervised state identification, successfully classifying three typical operating states: normal, high-load abnormal, and extreme load. The clustering silhouette coefficient reached 0.563, significantly outperforming K-means and hierarchical clustering. Safety evaluation results indicate that all relevant indicators meet international standards. The proposed method requires no labeled samples and offers strong physical interpretability and engineering applicability, providing effective support for turnout condition awareness and predictive maintenance. Full article