Search Results (22)

The intelligent bearing with an embedded sensor is a key technology to realize the running state monitoring of railway locomotive axle box bearings at the source end. To investigate the mechanical properties of axle box bearings with embedded sensor slots, based on nonlinear Hertzian contact theory and the bearing fatigue life theory, a mechanical equivalent analysis model with a virtual mandrel is established for double-row tapered roller bearings used in axle boxes with sensor-embedded slots, which integrally considers the effects of external forces. After verifying the mesh independence before and after embedding the sensor slots, the contact load of tapered rollers calculated by the mechanical model is compared with the theoretical solution based on Hertz contact which verifies the validity of the model from the perspective of contact load. The results show that adjusting the grooving depth and axial position has a significant effect on the local stress peak, and an excessive grooving depth or inappropriate axial position will trigger fatigue damage. This study provides a theoretical basis for analyzing the mechanical characteristics of sensor-embedded slots used in railway locomotive axle box bearings. Full article

The high-amplitude and high-frequency wheel–rail impacts excited by wheel flat result in severe contact friction in axle-box bearing (ABB), and the friction can cause a temperature rise or even structural damage of ABB. To this end, a thermal analysis model considering the vehicle operation environment is proposed to analyze the temperature characteristics of ABB. Various coupling dynamics effects between the vehicle–track system and ABB are synergistically integrated. The heat conduction, convection, and radiation between the various components of ABB are also integrated into the thermal analysis modeling. By the field tests, the accuracy of the ABB thermal model is validated. The results obtained through the model simulation show that the ABB temperature increases with vehicle speed and wheel flat length, and the ABB temperature at the outside row is higher than that at the inside row. Moreover, the temperature of the roller, cage, inner ring, outer ring, and axle box increases following an ascending order. Specifically, the temperature at the small end of the roller is higher than that at the large end. The findings of this study can provide engineering guidelines for the condition monitoring of ABBs. Full article

The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box bearings for fault diagnosis, a method for extracting the fault characteristic frequency based on principal component analysis (PCA) fusion and the optimal bandpass filtered denoising signal analytic energy operator (AEO) demodulation spectrum is proposed in this paper. PCA is used to measure the dimension reduction and fusion of three-direction vibration acceleration, reducing the interference of irrelevant noise components. A new type of multi-channel bandpass filter bank is constructed to obtain filtering signals in different frequency intervals. A new, improved average kurtosis index is used to select the optimal filtering signals for different channel filters in a bandpass filter bank. A dimensionless characteristic index characteristic frequency energy concentration coefficient (CFECC) is proposed for the first time to describe the energy prominence ability of characteristic frequency in the spectrum and can be used to determine the bearing fault type. The effectiveness and applicability of the proposed method are verified using the simulation signals and experimental signals of four fault bearing test cases. The results demonstrate the effectiveness of the proposed method for fault diagnosis and its advantages over other methods. Full article

In light of the issue that the vibration signal from an axle-box bearing collected during the operation of an electric multiple unit (EMU) is seriously polluted by background noise, which leads to difficulty in identifying fault characteristic frequency, this paper proposes a resonance-based sparse signal decomposition (RSSD) and variational mode decomposition (VMD) method based on sparrow search algorithm (SSA) optimization to extract the fault characteristic frequency of the bearing. Firstly, the RSSD method is utilized to decompose the signal based on the obtained optimal combination of quality factors, resulting in the optimal low-resonance component with periodic fault information. Then, the VMD method is performed on this low-resonance component. The parameter combinations for both methods are optimized utilizing the SSA method. Subsequently, envelope demodulation is applied to the intrinsic mode function (IMF) with maximum kurtosis, and fault diagnosis is achieved by comparing it with the theoretical fault characteristic frequency. Finally, experimental validation and comparison are conducted by utilizing simulated signals and example signals. The results demonstrate that the proposed method extracts more obvious periodic fault impact components. It effectively filters out the interference of complex noise and reduces the blindness of setting weights on parameters due to human experience, indicating excellent adaptability and robustness. Full article

Train axlebox bearings are subject to harsh service conditions, and the difficulty of diagnosing compound faults has brought greater challenges to the maintenance of high–quality train performance. In this paper, based on the traditional symplectic geometry mode decomposition (SGMD) algorithm, a maximum spectral coherence signal reconstruction algorithm is proposed to extract the intrinsic connection between the SGMD components with the help of the frequency domain coherence idea and reconstruct the key signal components so as to effectively improve the extraction of composite fault features of axlebox bearings under different speed conditions. Firstly, based on the traditional SGMD algorithm, the vibration signal of the axle box is decomposed to extract its symplectic geometry components (SGCs). Secondly, the spectral coherence coefficient between the SGCs is calculated, and the signal in which the maximum value is located is taken as the key component for the additive reconstruction Finally, the envelope spectrum is used to extract the reconstructed signal fault features. The inner race, outer race, and compound bearing failure vibration signal acquisition under different speed conditions were carried out on the equal scale axlebox bearing failure simulation test bench, and the effectiveness of the proposed algorithm was verified based on the axlebox vertical acceleration signal. Full article

The axle box bearing is a crucial component of high-speed electric multiple units (EMU) and is exposed to harsh working conditions, making it susceptible to subsurface-induced rolling contact fatigue (RCF) under long-term alternating stress. The objective of this paper is to develop a damage-coupled elastic–plastic constitutive model that can accurately predict the RCF life of EMU axle box bearings made from AISI 52100 bearing steel. The total damage is divided into elastic damage related to the shear stress range and plastic damage associated with plastic deformation. Material parameters are determined based on experimental data from the literature, and validation is conducted to ensure the validity of the model. Finally, the RCF behavior of the EMU axle box bearing, including crack initiation, crack propagation, and spalling, is simulated, and reasonable results are obtained. This study provides valuable insights into the RCF behavior of EMU axle box bearings and contributes to the accurate prediction of the fatigue life. Full article

Three current paths are proposed, and theoretical analysis and laboratory tests are carried out to investigate the root causes of bearing failure in IGBT inverter-fed locomotives and EMUs. The three types of current paths that run through the drive unit bearings and axle box bearings used on EMUs and electric locomotives are classified as the primary side current path, the main traction system current path, and the current path between the vehicles of the EMU or electric locomotive and the vehicles it hauls. The research found that the EDM current path in the main traction system caused by common mode voltage is distinguished as the main cause resulting in the failure of the bogie motor bearings or the bearings of the load connected to the motor shaft. The cause of common mode voltage is analyzed, and the thresholds of current density and voltage without causing bearing damage are analyzed and presented. The lab tests carried out on the bearings on the main traction system’s current path verified that the current path does exist. The proof to identify electric erosion, such as craters and washboards, and corresponding measures to prevent the failure of bogie bearings are proposed. Further research about the other two current paths is urgent and necessary. 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

Several aspects must be considered in the design of rolling bearing cages. One of the most important considerations relates to studying and developing a stationary approach for solving problems of heat and mass transfer during convection. In this context, this paper proposes, among other achievements, the development and validation of a model of heat generation that is used, as the basis for an energy-efficient cage design in the context of the roller bearings of axle boxes for rail transport. The forces of interaction of the cage with the bearing parts are determined. The energy-efficient design of the cage is performed with modified friction surfaces in the form of convex contours of the pockets and micro-hollows on the surfaces of the pockets and support rings. On the basis of a flat model, of the interaction between the cage and the bearing parts, the pressure forces on the driving and driven rolling elements in the zone of radial loading are determined. The frictional moment of the bearing has been determined based on the integral design of the cage without taking into account lubrication during the interaction of the cage with the jumpers and with the sides of the basing ring. The calculation of the temperature gradient with standard and improved designs of bearing cages has been performed while taking air blowing into account; results showed a decrease in the average level and growth rate of the bearings’ temperature gradient with an energy-efficient cage design. Based on the obtained results, and on the developed heat generation model, a systematic approach for energy-efficient design of rolling bearing cages is proposed. The proposed approach, as well as the respective developed models, were validated by obtaining and analyzing the experimental results. Full article

The axle box bearing is one of the core rotating components in high-speed trains, having served in complex working conditions for a long time. With the fault feature extraction of the vibration signal, the noise interference caused by the interaction between the wheels and rails becomes apparent. Especially when there is a shortwave defect in the rail, the interaction between wheels and rails will produce high-amplitude impulse interference. To solve the problem of the collected vibration signals of axle box bearings containing strong noise interference and high amplitude impact interference caused by rail shortwave irregularities, this paper proposes a method based on pre-identification via singular value decomposition technology to select the signals in sections and filter the noise, followed by feature extraction and fault diagnosis. The method is used to analyze the axle box bearing fault simulation signal and the weak fault signal collected by the railway bearing comprehensive experimental platform, and these signals are then compared with the random screening signal and the manual screening signal to verify the effectiveness of the method. Full article

A three-dimensional vehicle-axle box bearing coupling model is established. The model can calculate the contact force in three directions and obtain the dynamic response of axle box bearing under the real vehicle running environment. The load distribution on the double row tapered roller bearing and the vehicle is analyzed, and the co-simulation is conducted by comprehensively considering the force transmission between vehicle and bearing. Taking into account the great impact of defects on the bearing, three different types of bearing defects are added into the model, respectively. The simulation results verify the effectiveness of the model. The model is also verified by using the rolling and vibrating test rig of single wheelset. It is concluded that the simulation results show good agreement with experimental results. The influence of track irregularity on the system motion state is studied by using axis trajectory and vibration RMS (Root Mean Square value). The results show that the influence of track irregularity and wheel flat scar on axle box bearing cannot be ignored. The RMS of acceleration will change greatly due to the existence of defects. Wheel flat scar will greatly interfere with the extraction of bearing defect, but it can be selected at high speed and low frequency to monitor the existence of wheel flat scar, and select low speed and high frequency to monitor the existence of bearing defect. The research results are helpful to the detection of wheel flat scar and axle box bearing defect. Full article

Precise evaluation for flexural ultimate capacity of bridges which are subjected to the collision of over-height trucks is essential for making decisions on corresponding maintenance, strengthening or replacement. When the span of a cross-line continuous bridge with a double-box girder was hit by an overly high vehicle, the concrete floor of one girder was severely damaged, and part of the prestressed strands and reinforcements in the girder were broken. After the double-box girder was removed and separated into two single box girders, the ultimate flexural capacity of both box girders was studied by destructive tests, and a comparison was made between the damaged and undamaged girders. Moreover, finite element analysis was conducted to simulate the failure process. The results show that the flexural bearing capacity of the damaged box girder decreased by 33%, but it was still 1.07 times greater than the design bearing capacity, which basically meets the design requirements. Also, the damaged box girder showed a desirable serviceable limit state for three-axle vehicles and five-axle vehicles, but showed an undesirable serviceable limit state for six-axle vehicles. This study shows that repairing or strengthening the damaged span may be better than demolishing and rebuilding the whole superstructure bridge. Full article

Monitoring of railroad wagons is important for logistical processes, but above all for safety. One of the key parameters to be monitored is the temperature of the axle box and the bearings in the bogie. The problem with monitoring these parameters is the harsh environment and lack of power supply. In our research, we present a power supply system for a WSN node monitoring the bogie parameters. Knowing the operating conditions, we built a power supply system using a piezoelectric energy harvester. The harvester consists of three piezoelectric elements placed on a double arm pendulum beam. The circuit was modeled in the Comsol Multiphysics environment and then built and tested in laboratory conditions. After confirming energy efficiency, the system was tested on a freight car bogie during an 8 h trip. At typical car vibration frequencies (4–10 Hz), the system is able to generate 73 uW. Combined with an energy buffer of 1000 mAh (3.7 V), it can power a WSN node (based on the nRF5340 chip) for 13 years of operation. Full article

The axle box in the bogie system of subway trains is a key component connecting primary damper and the axle. In order to extract deep features and large-scale fault features for rapid diagnosis, a novel fault reconstruction characteristics classification method based on deep residual network with a multi-scale stacked receptive field for rolling bearings of a subway train axle box is proposed. Firstly, multi-layer stacked convolutional kernels and methods to insert them into ultra-deep residual networks are developed. Then, the original vibration signals of four fault characteristics acquired are reconstructed with a Gramian angular summation field and trainable large-scale 2D time-series images are obtained. In the end, the experimental results show that ResNet-152-MSRF has a low complexity of network structure, less trainable parameters than general convolutional neural networks, and no significant increase in network parameters and calculation time after embedding multi-layer stacked convolutional kernels. Moreover, there is a significant improvement in accuracy compared to lower depths, and a slight improvement in accuracy compared to networks than unembedded multi-layer stacked convolutional kernels. Full article

Wheel flats induce high-impact loads with relevance for the safety of the vehicle in operation as they can contribute to broken axles, hot axle boxes, and damaged rolling bearings and wheels. The high loads also induce damage in the track components such as rails and sleepers. Although this subject has been studied numerically and experimentally over the last few years, the wheel flat problem has focused on ballasted tracks, and there is a need to understand the phenomena also for slab tracks. In this research, a numerical approach was used to show the effects of the wheel flats with different geometric configurations on the dynamic behavior of a classical ballasted track and a continuous slab track. Several wheel flat geometries and different vehicle speeds were considered. The nonlinear Hertzian contact model was used because of the high dynamic variation of the interaction of the load between the vehicle and the rail. The results evidenced that, for the same traffic conditions, the dynamic force was higher on the slab track than on the ballasted one, contrary to the maximum vertical displacement, which was higher on the ballasted track due to the track differences regarding the stiffness and frequency response. The results are useful for railway managers who wish to monitor track deterioration under the regulatory limits. Full article