Search Results (15)

Tread braking is still extensively used on freight wagons due to lower purchasing and maintenance costs compared to disk braking. Cast iron brake blocks were replaced by composite materials (organic or sintered) that result in a lower wheel roughness, reducing rolling noise. Unfortunately, composite brake blocks have a lower thermal conductivity, negatively affecting the wheel mechanical behavior as the braking energy is almost entirely dissipated by the wheels, which are therefore subjected to higher temperatures. Mechanical properties of the wheel material, such as yield stress and Rolling Contact Fatigue (RCF) behavior, markedly decrease with temperature, resulting in higher wear rates and wheel tread damage. Contacted to analyze defects not clearly defined in the current regulations used for maintenance and inspections, the authors surveyed the literature and the technical documentation about tread-braked wheels. The paper provides an updated view about the state-of-the-art of the research on thermomechanical behavior of railway wheels and discusses the implication of the increased thermal stresses generated by composite brake blocks. Full article

Defective wheels pose a significant challenge in railway transportation, impacting operational performance and safety. Excessive traction and braking forces give rise to deviations from the intended conical tread shape, resulting in amplified vibrations and noise. Moreover, these deviations contribute to the accelerated damage of track components. Detecting wheel defects at an early stage is crucial to ensure safe and comfortable operation, as well as to minimize maintenance costs. However, the presence of various vibrations, such as those induced by the track, traction motors, and other rolling stock subsystems, poses a significant challenge for onboard detection techniques. These vibrations create difficulties in accurately identifying wheel defects in real-time during operational activities, often resulting in false alarms. This research paper aims to address this issue by using a hybrid deep learning-based approach for the accurate detection of various types of wheel defects using accelerometer data. The proposed approach aims to enhance wheel defect detection accuracy while considering onboard techniques’ cost-effectiveness and efficiency. A realistic simulation model of the railway wheelset is developed to generate a comprehensive dataset. To generate vibration data in various scenarios, the model is simulated for 20 s under different conditions, including one non-faulty scenario and six faulty scenarios. The simulations are conducted at different speeds and track conditions to capture a wide range of operating conditions. Within each simulation iteration, a total of 200,000 data points are generated, providing a comprehensive dataset for analysis and evaluation. The generated data are then utilized to train and evaluate a hybrid deep learning model, employing a multi-layer perceptron (MLP) as a feature extractor and multiple machine learning models (support vector machine, random forest, decision tree, and k-nearest neighbors) for performance comparison. The results demonstrate that the MLP-RF (multi-layer perceptron with random forest) model achieved an accuracy of 99%, while the MLP-DT (multi-layer perceptron with decision tree) model achieved an accuracy of 98%. These high accuracy values indicate the effectiveness of the models in accurately classifying and predicting the outcomes. The contributions of this research work include the development of a realistic simulation model, the evaluation of sensor layout effectiveness, and the application of deep learning techniques for improved wheel flat detections. Full article

In the existing rolling bearing performance degradation assessment methods, the input signal is usually mixed with a large amount of noise and is easily disturbed by the transfer path. The time information is usually ignored when the model processes the input signal, which affects the effect of bearing performance degradation assessment. To solve the above problems, an end-to-end performance degradation assessment model of railway axle box bearing based on a deep residual shrinkage network and a deep long short-term memory network (DRSN-LSTM) is proposed. The proposed model uses DRSN to extract local abstract features from the signal and denoises the signal to obtain the denoised feature vector, then uses deep LSTM to extract the time-series information of the signal. The healthy time-series signal of the rolling bearing is input into the DRSN-LSTM reconstruction model for training. Time-domain, frequency-domain, and time–frequency-domain features are extracted from the signal both before and after reconstruction to form a multi-domain features vector. The mean square error of the two feature vectors is used as the degradation indicator to implement the performance degradation assessment. Artificially induced defects and rolling bearings life accelerated fatigue test data verify that the proposed model is more sensitive to early failures than mathematical models, shallow networks or other deep learning models. The result is similar to the development trend of bearing failures. Full article

Rolling noise is a significant contributor to railway noise. Wheel and rail roughness are decisive for the emitted noise level. An optical measurement method installed on a moving train is suitable for closer monitoring of the rail surface condition. A measurement setup based on the chord method requires the sensors to be positioned in a straight line along the direction of measurement and in a stable lateral position. Measurements should always be performed within the shiny and uncorroded running surface, even when there are lateral movements of the train. In this study, concepts for the detection of the running surface and the compensation of lateral movements are investigated in a laboratory setting. The setup consists of a vertical lathe with a ring-shaped workpiece that incorporates an implemented artificial running surface. The detection of the running surface based on laser triangulation sensors and a laser profilometer is investigated. It is shown that the running surface can be detected using a laser profilometer that measures the intensity of the reflected laser light. It is possible to detect the lateral position and the width of the running surface. A linear positioning system is proposed to adjust the lateral position of the sensors based on the running surface detection of the laser profilometer. When the lateral position of the measuring sensor is disturbed by a movement with a wavelength of $18.85 \mathrm{m}$, the linear positioning system can keep the laser triangulation sensor inside the running surface for $98.44\%$ of the measured data points at a velocity of approximately $7.5 {\mathrm{km} \mathrm{h}}^{-1}$. The mean positioning error is $1.40 \mathrm{mm}$. By implementing the proposed system on the train, future studies can be conducted to examine the lateral position of the running surface as a function of the various operational parameters of the train. Full article

A large part of the noise emissions from rail traffic originates from rolling noise. This is significantly determined by the surface roughness of the wheel and the rail. To quantitatively assess the noise generation from the wheel–rail contact, it is necessary to measure the surface roughness of the rail network. Direct measurements via trolley devices are usually associated with the need for a free track and limitation in velocity. Indirect measurements of rail roughness, such as measuring axle-box accelerations, enable operation during regular passage but only estimate the acoustic roughness. In this study, the potential of an optical and consequently contact-free measurement method using laser triangulation sensors to measure rail roughness from the train is investigated. The approach can combine the advantage of operation during regular passage with the characteristics of a direct measurement, enabling large-scale monitoring of the rail network. A measurement run with a train was carried out on a meter-gauge track at speeds up to $80{ \mathrm{km} \mathrm{h}}^{-1}$ The results of the optical measurement approach were compared with a tactile reference measurement on the track. The results show good agreement of the new measurement setup for dry rail surface conditions at $50 {\mathrm{km} \mathrm{h}}^{-1}$, with a mean deviation of $1.48 \mathrm{dB}$. Full article

The research on wheel–rail relationship includes the basic theoretical models and corresponding numerical methods of wheel–rail in rolling contact, geometric parameter matching and material matching of them, friction and wear, wheel–rail rolling contact fatigue, wheel–rail adhesion and noise. They are also key theoretical and technical problems of the high-speed train/track coupling system. The basic theoretical models of wheel–rail in rolling contact and the corresponding numerical methods are the basis and one of the basic means for solving other wheel–rail relationship problems. The other is the experimental means. Moreover, the modeling and analysis of coupling behavior of the train and track can only be realized by means of the wheel–rail rolling contact mechanics model and its corresponding numerical method. This paper mainly discusses some research work and achievements on high-speed wheel–rail relationship problems since China opened a high-speed railway system on a large scale. The discussions in this paper include the classic wheel–rail rolling contact theoretical models (analytical forms) and the modern wheel–rail rolling contact theories (numerical methods), their advantages and disadvantages, their application and future development direction of them. The reviewed research progress on the other wheel–rail relationships mainly expounds the thorny problems of the wheel–rail relationship encountered in the operation of China’s high-speed railway, how to adopt new theoretical analysis methods, test means and take effective measures to solve these problems. It also includes research results of similar important reference values performed by international peer experts in related fields. Challenging and unsolved problems in high-speed wheel–rail relationship research are also reviewed in the full text. Full article

Railway transport is considered relatively environmentally friendly in terms of energy consumption and air pollution, but it is relatively unfriendly in terms of noise pollution. Noise and vibrations propagating to railroad surrounding areas are disturbing populations. In order to minimize this noise, legislation and regulations such as TSI NOI have been adopted and research of noise and vibrations generated by railway transport has been carried out. Such research has been carried out also by our team focused on experimental investigation of noise generated by railway wagons, in this particular case on tank wagons. We simulated the structural eigenfrequencies of both bogies and tanks using FEM models to find vibrations and corresponding noise levels generated by these vibrations. Theoretical results have been compared with results of measurements of noise generated by impact hammer and visualization of noise fields using a digital acoustic camera Soundcam. Based on the simulation and measurements, principal frequency noise domains of fundamental noise sources were determined—rolling (40–63 Hz), tank (200–1000 Hz), bogie (400–1600 Hz), and wheel (800–10,000 Hz). Measurements on the railway line under real operational conditions at two train speeds have been carried out, too, to see the actual external noise levels. Full article

For acoustic roughness monitoring of the railway network at train travelling speed, new direct measurement methods are required. Common direct measurement methods need the blocking of track sections, as they are based on manually operated devices. Indirect measurement methods such as accelerometer or microphone measurements can be installed on the train, but require a conversion of the obtained measurement data to rail roughness. Optical measurement methods allow a direct measurement from the moving train, even at higher speeds, due to the contact-free nature of the measurement. This paper investigates the influence of various disturbances on the measurement result, which are expected on the train. The frequently used chord method deploying laser triangulation sensors is used. Four sensors are integrated into the setup, thus providing the possibility to combine the results from four chord methods. The measurements of the optical system are compared with a tactile measurement of METAS (Swiss Federal Institute of Metrology) on a test bench equipped with a reference rail segment. It is shown that dust and water on the rail have a significant influence in the range of small wavelengths. Displacements and tilting of the sensor array, as well as vibrations, can be compensated to a certain level by the chord method, while a single sensor is significantly disturbed. The combination of four different chord lengths and selection of the theoretically optimal method for each one-third octave band shows an improvement of the measurement result. Based on the observations made, recommendations for practical tests on the train are concluded. Full article

Based on elastic mechanics, the fluid–structure coupling theory and the finite element method, a high-speed railway wheel-rail rolling-aerodynamic noise model is established to realize the combined simulation and prediction of the vibrations, rolling noise and aerodynamic noise in wheel-rail systems. The field test data of the Beijing–Shenyang line are considered to verify the model reliability. In addition, the directivity of each sound source at different frequencies is analyzed. Based on this analysis, noise reduction measures are proposed. At a low frequency of 300 Hz, the wheel-rail area mainly contributes to the aerodynamic noise, and as the frequency increases, the wheel-rail rolling noise becomes dominant. When the frequency is less than 1000 Hz, the radiated noise fluctuates around the cylindrical surface, and the directivity of the sound is ambiguous. When the frequency is in the middle- and high-frequency bands, exceeding 1000 Hz, both the rolling and total noise exhibit a notable directivity in the directions of 20–30° and 70–90°, and thus, noise reduction measures can be implemented in these directions. Full article

In this work, the problematic identification of the main sources of noise occurring from the exploitation of railway vehicles moving at a speed of 200 km/h were analyzed. Within the conducted experimental research, the testing fields were appointed, measurement apparatus selected, and a methodology for conducting measurements was defined, including the assessment of noise on a curve and straight track for electric multiple units of the so-called Pendolino, an Alstom type ETR610 series ED25 train. The measurements were made using a microphone camera Bionic S-112 at a distance of 22 m from the track axis. As a result of the conducted experimental research, it was indicated that the noise resulting from vibrations arising at the wheel-rail contact (rolling noise) was the dominant source of sound. Full article

Contact and friction between wheel and rail during train operation is the main cause of the rolling noise for which railways are known. Therefore, it is necessary to accurately measure the surface roughness of wheels and rails to monitor railway noise and predict noise around tracks. Conventional systems developed to measure surface roughness have large deviations in measured values or low repeatability. The recently developed automatic mobile measurement platform known as Auto Rail Checker (ARCer) uses three displacement sensors to reduce measurement deviation and increase the accuracy of existing systems. This paper proposes enhancing the chord offset synchronization algorithm applied to the existing ARCer for high measurement precision with only two displacement sensors. As a result, when the two sensor-based measurement algorithm was applied, the spectrum level at $\lambda$ = 0.314 m, the wavelength amplification associated with wheel diameter, was reduced to at least 6 dB in comparison with that of the three sensors based algorithm. We also verified the accuracy of the proposed batch algorithm through a field test on an operating rail track with a corrugated rail surface. Full article

The rolling bearing is a key component of the bogie of the rail train. The working environment is complex, and it is easy to cause cracks and other faults. Effective rolling bearing fault diagnosis can provide an important guarantee for the safe operation of the track while improving the resource utilization of the rolling bearing and greatly reducing the cost of operation. Aiming at the problem that the characteristics of the vibration data of the rolling bearing components of the railway train and the vibration mechanism model are difficult to establish, a method for long-term faults diagnosis of the rolling bearing of rail trains based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm is proposed. Firstly, the sliding time window segmentation algorithm of exponential smoothing is used to segment the rolling bearing vibration data, and then the segmentation points are used to construct the localized features of the data. Finally, an Improved AdaBoost Algorithm (IAA) is proposed to enhance the anti-noise ability. IAA, Back Propagation (BP) neural network, Support Vector Machine (SVM), and AdaBoost are used to classify the same dataset, and the evaluation indexes show that the IAA has the best classification effect. The article selects the raw data of the bearing experiment platform provided by the State Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University and the standard dataset of the American Case Western Reserve University for the experiment. Theoretical analysis and experimental results show the effectiveness and practicability of the proposed method. Full article

It is necessary to measure accurately the rolling noise generated by the friction between wheels and rails in railway transport systems. Although many systems have recently been developed to measure the surface roughness of wheels and rails, there exist large deviations in measurements between each system whose measuring mechanism is based on a single sensor. To correct the structural problems in existing systems, we developed an automatic mobile measurement platform, named the Automatic Rail Checker (ARCer), which measures the acoustic roughness of a longitudinal railhead profile maintaining a constant speed. In addition, a new chord offset synchronization algorithm has been developed. This uses three displacement sensors to improve the measuring accuracy of the acoustic roughness of a longitudinal railhead profile, thereby minimizing the limitations of mobile platform measurement systems and measurement deviation. We then verified the accuracy of the measurement system and the algorithm through field tests on rails with different surface wear conditions. Full article

Rail surface defects are a serious concern for railway infrastructure managers all around the world. They lead to poor ride quality due to excess vibration and noise; in rare cases, they can result in a broken rail and a train derailment. Defects are typically classified as ‘rail studs’ when they initiate from the white etching layer, and ‘rail squats’ when they initiate from rolling contact fatigue. This paper presents a novel investigation into rail squat initiation and growth simulations using peridynamic theory. To the best of the authors’ knowledge, no other comprehensive study of rail squats has been carried out using this approach. Peridynamics are well-suited for fracture problems, because, contrary to continuum mechanics, they do not use partial-differential equations. Instead, peridynamics use integral equations that are defined even when discontinuities (cracks, etc.) are present in the displacement field. In this study, a novel application of peridynamics to rail squats is verified against a finite element solution, and the obtained simulation results are compared with in situ rail squat measurements. Some new insights can be drawn from the results. The outcome exhibits that the simulated cracks initiate and grow unsymmetrically, as expected and reported in the field. Based on this new insight, it is apparent that peridynamic modelling is well-applicable to fatigue crack modeling in rails. Surprisingly, limitations to the peridynamic analysis code have also been discovered. Future work requires finding an adequate solution to the matter-interpenetration problem. Full article

A focus of the railway industry over the past decades has been to research, find and develop methods to mitigate noise and vibration resulting from wheel/rail contact along track infrastructure. This resulted in a wide range of abatement measures that are available for today’s engineers. The suitability of each method must be analysed through budget and timeframe limitations, which includes building, maintenance and inspection costs and time allocation, while also aiming at delivering other benefits, such as environmental impact and durability of infrastructure. There are several situations that need noise and vibration mitigation methods, but each design allocates different priorities on a case-by-case basis. Traditionally, the disturbance caused by railways to the community are generated by wheel/rail contact sound radiation that is expressed in different ways, depending on the movement of the rolling stock and track alignment, such as rolling noise, impact noise and curve noise. More specifically, in special trackworks such as turnouts (or called “switches and crossings”), there are two types of noise that can often be observed: impact noise and screeching noise. With respect to the screeching (or flanging), its mitigation methods are usually associated with curve lubrications. In contrast, the impact noise emerges from the sound made by the rolling stock moving through joints and discontinuities (i.e., gaps), resulting in various noise abatement features to minimise such noise impact. Life cycle analysis is therefore vital for cost efficiency benchmarking of the mitigation methods. The evaluation is based on available data from open literature and the total costs were estimated from valid industry reports to maintain coherency. A 50-year period for a life cycle analysis is chosen for this study. As for the general parameters, an area with a high density of people is considered to estimate the values for a community with very strict limits for noise and vibration. Full article