Abstract In this paper, the corrosion failure mechanism of bearing outer rings for railway freight car wheelset and the wear performance of coatings were systematically analyzed and tested. The results show that the microcells formed by martensite and Fe 3 C in the matrix structure accelerate electrochemical corrosion, which is mainly dominated by Cl -induced localized corrosion. The main components in the rust layer, γ-FeOOH/γ-Fe 2 O 3 , have a loose and porous structure, making it difficult to effectively block the penetration of corrosive media and forming a cycle of "corrosion-infiltrationrecorrosion". In the wear performance tests of different coatings, the weight loss rate of the epoxy coating after friction reaches 0.0616%, that of the phosphating film is less than 0.0019%, and although the thermal spray coating has a higher friction coefficient, its weight loss rate is close to zero, making it suitable for high-load and severe-wear scenarios.

Multi-Factor Coupling Corrosion Mechanisms and Protective Strategies of Chinese Railway Freight Cars

Increasing the corrosion resistance of the center plate unit in railway freight car

The prediction of Remaining Useful Life (RUL) constitutes a vital aspect of Prognostics and Health Management (PHM), providing capabilities for the assessment of mechanical component health status and prediction of failure instances. Recent studies on feature extraction, time-series modeling, and multi-task learning have shown remarkable advancements. However, most deep learning (DL) techniques predominantly focus on unimodal data or static feature extraction techniques, resulting in a lack of RUL prediction methods that can effectively capture the individual differences among heterogeneous sensors and failure modes under complex operational conditions. To overcome these limitations, an adaptive RUL prediction framework named ADAPT-RULNet is proposed for mechanical components, integrating the feature extraction capabilities of attention-enhanced deep learning (DL) and the decision-making abilities of deep reinforcement learning (DRL) to achieve end-to-end optimization from raw data to accurate RUL prediction. Initially, Functional Alignment Resampling (FAR) is employed to generate high-quality functional signals; then, attention-enhanced Dynamic Time Warping (DTW) is leveraged to obtain individual degradation stages. Subsequently, an attention-enhanced of hybrid multi-scale RUL prediction network is constructed to extract both local and global features from multi-format data. Furthermore, the network achieves optimal feature representation by adaptively fusing multi-source features through Bayesian methods. Finally, we innovatively introduce a Deep Deterministic Policy Gradient (DDPG) strategy from DRL to adaptively optimize key parameters in the construction of individual degradation stages and achieve a global balance between model complexity and prediction accuracy. The proposed model was evaluated on aircraft engines and railway freight car wheels. The results indicate that it achieves a lower average Root Mean Square Error (RMSE) and higher accuracy in comparison with current approaches. Moreover, the method shows strong potential for improving prediction accuracy and robustness in varied industrial applications.

A wheel flat is the most common fault of a railway freight car, a type of complex transport equipment. A wheel flat will cause continuous regular impact on the rail, damage the rail and the railway structure, affecting the safety and stability of rail transport. This article studied the relationship between wheel flats and wheel–rail impacts using multi-body dynamics simulation through SIMPACK and, through a field test, validates the detection of a flat wheel. The results show that using the simulation method can obtain similar data to the measured wheel–rail force in the wayside detection device. The simulation data show that the data collected by 14 shear vertical force acquisition channels can completely cover the wheel surface of the heavy-duty railway 840 mm diameter wheel. According to the flat length-speed-impact diagram, the mapping relationship can be fitted using polynomial regression. Based on the measured wheel–rail impact forces, the size of wheel flats can then be deduced from this established mapping relationship. Through a field test, the detection method has been validated.

PurposeWeathering steel has excellent resistance to atmospheric corrosion, but still faces complex environmental corrosion problems during long-term operation. This paper mainly studies the corrosion problem of weather resistant steel materials for railway freight car bodies with a load capacity of 70 tons.Design/methodology/approachThe paper analyzes the corrosion characteristics of weather resistant steel materials for truck bodies through macroscopic and microscopic methods including metallographic microscopy, scanning electron microscopy, energy dispersive spectroscopy and X-ray diffraction. Electrochemical analysis shows that the rust layer on the surface of weathering steel changes the surface state of the material, and also proves that weathering steel used in trucks undergoes electrochemical corrosion under atmospheric corrosion. At the same time, ion chromatography technology is used to study the corrosive ions mainly present in the residual liquid and foam solution inside the vehicle body.FindingsThe corrosion of truck body materials is mainly electrochemical corrosion, and the corrosion of door materials is more obvious than that of other parts. The corrosion products are mainly Fe oxides and hydroxides. There are high concentrations of Cl – and SO42– ions in the residual liquid and foam solution at the bottom of the freight car, which are the main factors causing corrosion of the railway freight car body.Originality/valueThe foam adhesive around the door panel is in a moist state for a long time, and corrosive ions will accelerate the electrochemical corrosion of the weather resistant steel material of the door panel. Therefore, the corrosion of the cargo door panel is more severe than other components.

Abstract The research focuses on forecasting prospective technical characteristics of freight cars that meet the evolving demands of the modern railway industry. An analysis of the structure of the existing freight car fleet on Ukrainian railways highlights a significant need for specialized rolling stock for transporting grain crops. Under current circumstances, identifying methodologies for determining the technical characteristics of freight cars has become essential. One of the most relevant and effective approaches involves forecasting key technical characteristics based on the modelling processes of freight cars under limited resource conditions, employing a continuous exponential function. Using the growth rate function of the projected freight car metric, a logistic characteristic is determined, which assumes an exponential form under unlimited resources and a linear form under constrained conditions. Based on the current needs of the domestic railway freight car fleet, the forecasting of prospective characteristics will be conducted using the example of grain hopper cars and relying on statistical information. According to the proposed methodology and based on the obtained data, the expected value of the specific volume, which is the ratio of the freight car body to its load capacity, was determined, taking into account the degree of reliability of the forecast and the calculation of the forecast error. The calculations demonstrate high accuracy of the forecast achieved by this methodology. As a result, it becomes possible to evaluate the required freight car characteristics for the projected year with a high degree of reliability.

Railwayfreight cars operating in heavy-load and complex outdoor environments are frequently subject to adverse conditions such as haze, temperature fluctuations, and transmission interference, which significantly degrade the quality of the acquired images and introduce substantial noise. Furthermore, the structural complexity of freight cars, coupled with the small size, diversity, and complex structure of defect areas, poses serious challenges for image denoising. Specifically, it becomes extremely difficult to remove noise while simultaneously preserving fine-grained textures and edge details. These challenges distinguish railway freight car image denoising from conventional image restoration tasks, necessitating the design of specialized algorithms that can achieve both effective noise suppression and precise structural detail preservation. To address the challenges of incomplete denoising and poor preservation of details and edge information in railway freight car images, this paper proposes a novel image denoising algorithm named the Nonlinear Activation-Free Network based on Multi-Scale Edge Enhancement and Fusion (NAF-MEEF). The algorithm constructs a Multi-scale Edge Enhancement Initialization Layer to strengthen edge information at multiple scales. Additionally, it employs a Nonlinear Activation-Free feature extractor that effectively captures local and global image information. Leveraging the network's multi-branch parallelism, a Multi-scale Rotation Fusion Attention Mechanism is developed to perform weight analysis on information across various scales and dimensions. To ensure consistency in image details and structure, this paper introduces a fusion loss function. The experimental results show that compared with recent advanced methods, the proposed algorithm has better noise suppression and edge preservation performance. The proposed method achieves significant denoising performance on railway freight car images affected by Gaussian, composite, and simulated real-world noise, with PSNR gains of 1.20 dB, 1.45 dB, and 0.69 dB, and SSIM improvements of 2.23%, 2.72%, and 1.08%, respectively. On public benchmarks, it attains average PSNRs of 30.34 dB (Set12) and 28.94 dB (BSD68), outperforming several state-of-the-art methods. In addition, this method also performs well in railway image dehazing tasks and demonstrates good generalization ability in denoising tests of remote sensing ship images, further proving its robustness and practical application value in diverse image restoration tasks.

Locking state recognition is one of the key tasks of railway freight monitoring. However, accurate localization and recognition of small locking mechanisms remain major challenges. Current approaches that focus on existing object recognition methods lead to high false detection and miss rates. This paper introduces TrainNet for efficient locking state detection for Open-top wagon doors. A dataset was collected using a robot with a camera to validate our method. We designed an efficient layer aggregation network (ELAN)-S module in our TrainNet, which can be used with YOLOv7. The module efficiently extracts curvilinear features and is integrated into the backbone feature extraction network to enhance the feature representation capability. An LSKCSPC module is also introduced to capture a dynamic receptive field, enabling TrainNet to adjust its receptive field dynamically to the scale of the object, improving its feature representation capacity. Furthermore, the detection head for small-scale objects is redesigned, the feature layer size is increased to enhance the ability to extract and detect fine-grained features. Finally, the loss function is modified to a dynamic fusion-based CNIOU, which reduces the sensitivity of original loss to small objects and improves performance. Experimental results show that the algorithm achieves a mean average precision (mAP50) of 91.5%, which is a 3.6% improvement over the baseline YOLOv7 algorithm. The model weight file size of new algorithm is 60.2MB with 19.5% reduction compared to the baseline. The proposed method achieves 91.5% for detecting the lock status of railway freight open-top wagon side doors, while reducing the complexity of the original algorithm and achieving a real-time detection speed of 39.8fps, meeting the requirements for practical application. The algorithm also exhibits good robustness as demonstrated by experiments on the WiderPerson dataset.

Cause Analysis and Improvement of Brake Branch Pipe Failure of Railway Freight Car

EdgeAD: Unsupervised Learning Model Based on Prior Knowledge Enhanced Image Anomaly Detection of Heavy Railway Freight Cars

PurposeThe brake pipe system was an essential braking component of the railway freight trains, but the existing E-type sealing rings had problems such as insufficient low-temperature resistance, poor heat stability and short service life. To address these issues, low-phenyl silicone rubber was prepared and tested, and the finite element analysis and experimental studies on the sealing performance of its sealing rings were carried out.Design/methodology/approachThe low-temperature resistance and thermal stability of the prepared low-phenyl silicone rubber were studied using low-temperature tensile testing, differential scanning calorimetry, dynamic thermomechanical analysis and thermogravimetric analysis. The sealing performance of the low-phenyl silicone rubber sealing ring was studied by using finite element analysis software abaqus and experiments.FindingsThe prepared low-phenyl silicone rubber sealing ring possessed excellent low-temperature resistance and thermal stability. According to the finite element analysis results, the finish of the flange sealing surface and groove outer edge should be ensured, and extrusion damage should be avoided. The sealing rings were more susceptible to damage in high compression ratio and/or low-temperature environments. When the sealing effect was ensured, a small compression ratio should be selected, and rubbers with hardness and elasticity less affected by temperature should be selected. The prepared low-phenyl silicone rubber sealing ring had zero leakage at both room temperature (RT) and −50 °C.Originality/valueThe innovation of this study is that it provides valuable data and experience for the future development of the sealing rings used in the brake pipe flange joints of the railway freight cars in China.

To address the issue of accurately extracting fault characteristic information of railway freight car bearings under noisy conditions, this paper proposes a fault diagnosis method based on Adaptive Chirp Mode Decomposition (ACMD) and an optimized Maximum Correlation Kurtosis Deconvolution (MCKD) using a Sparrow Search Algorithm Combining Sine-Cosine and Cauchy Mutation (SCSSA). Firstly, ACMD is used to decompose and reconstruct the original fault signal to obtain several Intrinsic Mode Functions (IMFs). Then, the IMFs are filtered according to the Gini coefficient indicator, with the IMF having the largest Gini coefficient selected as the optimal component. Secondly, the SCSSA is employed to iteratively optimize the filter length L, fault signal period T, and displacement parameter M in the MCKD algorithm, determining the optimal parameter combination for MCKD. This avoids the limitations of manual settings and enhances the accuracy of fault diagnosis. The optimized MCKD is then applied to the optimal component, and deconvolution is performed using maximum correlation kurtosis as the criterion to extract fault characteristic information through its envelope spectrum. To verify the effectiveness and generalizability of the proposed method, simulations, experimental signals from the Case Western Reserve University Bearing Center, and actual measured signals from railway freight car bearing 353130B are used to analyze inner ring faults. The experimental results demonstrate that the method can accurately extract fault characteristic information of railway freight car bearings under noise interference and identify the fault type.

A derailment detection algorithm for railway freight cars based on micro inertial measurement units was designed to address the complex issue of the disassembly and assembly of derailment braking devices. Firstly, a horizontal attitude measurement model for freight cars was established, and attitude measurement algorithms based on gyroscopes and accelerometers were introduced. Subsequently, a high-precision attitude measurement algorithm based on variational Bayesian Kalman filtering was proposed, which used acceleration information as the observation data to correct attitude errors. In order to improve the accuracy of derailment detection, a dual-model instantaneous attitude difference measurement technique was further proposed. In order to verify the effectiveness of the algorithm, offline data from simulation experiments and in-vehicle experiments were used to validate the proposed algorithm. The results showed that the proposed algorithm can effectively improve the measurement accuracy of horizontal attitude changes, reducing the error by 89% compared to pure inertial attitude calculation, laying a technical foundation for improving the accuracy of derailment detection.

Pakistan has long struggled with a persistent balance of payments deficit, primarily due to merchandise exports being significantly lower than imports. This issue is exacerbated by the country’s inability to move up the quality ladder leaving its exports concentrated in low value-added products exacerbating the balance of payment deficit problem. Recognizing the critical need to expand and diversify its export base, this study utilizes the product space network and connectedness framework from the Atlas of Economic Complexity (AEC) to identify potential high value-added products for export growth. The analysis suggests adding products like coke from coal (HS 2704), railway freight cars (HS 8606), Malt (HS 1107), chemical wood pulp (HS 4703), unwrought nickel (HS 7502) to Pakistan’s export basket. Expanding exports of existing products such as original sculptures (HS 9703), fishing rods, decoy birds (HS 9507), footwear (HS 6404), stranded wire, cables (HS 7413)’ is also recommended. We also estimated the potential gain in the export revenues if Pakistan starts exporting a subset of the products identified. Our estimates indicate that exporting new primary connections could increase merchandise export revenue by 5.54%, while expanding high value-added products could boost revenues by nearly 27.7 % to US$ 37 billion. Policymakers should focus on these products for an export-oriented industrial strategy. Trade officers and negotiators should link potential foreign buyers with Pakistani producers to capitalize on these opportunities. Prioritizing these products in trade agreements and tariff discussions can also support export growth, helping to address Pakistan's balance of payments deficit and foster sustainable economic development.

Abstract Taking the key welds of the heavy-duty gondola carbody as the research object, first, the fatigue assessment of the carbody is carried out based on the quasi-static the main S–N curve method, and the quasi-static evaluation method is discussed; second, the typical welds are selected. Based on the full-size bench physical test and virtual test of railway freight carbody, the quasi-static method is discussed; finally, the line test is used to verify the results. The results show that: (1) among all the load conditions in the quasi-static analysis of railway freight cars, the damage of the core plate needs to be considered according to different parts of the carbody, and this condition is effective for the evaluation of the bearing area of the linear load transfer of the carbody for the welds with nonlinear load transfer such as floor, the stress response caused by core plate load has the problem of over-estimation of damage. (2) The lap weld of floor and beam is less damaged under heavy load, but the stress response is larger in empty state, which indicates that the vibration effect should be considered in the design. (3) The virtual test can make up for the problem of traditional quasi-static analysis. The combination of the two can better realize the fatigue reliability evaluation of carbody.

PurposeThe purpose of this paper is to investigate the non-linear characteristics and stability of the rolling bearing–axle coupling system under the excitation of the axle/wheel speed of railway freight cars, so as to put forward a rationale for judging the vibration law and running stability of railway freight wagon.Design/methodology/approachConsidering the effects of eccentric force of the railway wagon axle, the non-linear resistance of the wagon and non-linear support forces of axle box rolling bearings, a centralized mass model of rolling bearing-axle coupling system of railway freight wagon is presented on the basis of the theory of rotor dynamics and non-linear dynamics. Then the Runge-Kutta method is adopted to solve the non-linear response of the proposed system, and numerical simulation including bifurcation diagrams, axis trajectory curves, phase plane plots, Poincaré sections and amplitude spectras are analysed when the axle rotating speed is changed. Meantime, the relation curve between Floquet multiplier and axle rotating speed, which affects the stability of coupling system, is plotted by numerical method based on the Floquet theory and method.FindingsThe simulation results of the dynamic model reveal the abundant dynamic behaviour of the coupling system when the axle rotating speed changes, including single period, quasi period, multi-period and chaotic motion, as well as the evolution law from multi-period motion to chaotic motion. And especially, the bearing–axle coupling system is in stable state with a single period motion when the axle rotating speed changes from 410 rpm to 510 rpm, in which the running speed of railway freight wagon is changed from 62 km/h to 80 km/h, the vibration displacement of the coupling system in X direction is between 1.2 mm and 1.8 mm, and the vibration displacement of the coupling system in Y direction is between 1.0 mm and 1.45 mm. Meanwhile, the influence law of axle rotating speed on the stability is obtained by comparing the bifurcation diagram and Floquet multiplier graph of the coupling system.Originality/valueThe numerical simulation data obtained in this study can provide a theoretical evidence for designing the running speed of railway freight wagon, utilizing or controlling the non-linear dynamic behaviours of the proposed coupling system, and ensuring the stability of railway freight wagons.

Identifying and detecting the loading size of heavy-duty railway freight cars is crucial in modern railway freight transportation. Due to contactless and high-precision characteristics, light detection and ranging-assisted unmanned aerial vehicle stereo vision detection is significant for ensuring out-of-gauge freight transportation security. However, the precision of unmanned aerial vehicle flight altitude control and feature point mismatch significantly impact stereo matching, thus affecting the accuracy of railway freight measurement. In this regard, the altitude holding control strategy equipped with a laser sensor and SURF_rBRIEF image feature extraction and matching algorithm are proposed in this article for railway freight car loading size measurement. Moreover, an image segmentation technique is used to quickly locate and dismantle critical parts of freight cars to achieve a rapid 2-dimension reconstruction of freight car contours and out-of-gauge detection. The robustness of stereo matching has been demonstrated by external field experiment. The precision analysis and fast out-of-gauge judgment confirm the measurement accuracy and applicability.

On the one hand, considering that the traditional fatigue method of railway freight cars is based on damage as a parameter, the influence of stress waveform cannot be considered. On the other hand, physical experiments have the characteristics of lag, long period, and high cost. The full-scale physical test and virtual test of car body are carried out. First of all, the data processing method of small deletion and the inverse problem load acquisition method based on data to data are proposed. Secondly, the dynamic stress calculation method with the bench as the boundary is proposed. Finally, taking the obtained load as the input of the physical and virtual bench, a new fatigue test method for simulating the running attitude of the car body line is completed. The acceleration RMS error of the C70E gondola body is less than 6%, the stress RMS is less than 13%, and the equivalent mileage is 3.125 million highway test results show that the car meets the life requirements of the car body. The inverse problem analysis results of virtual and physical tests are basically consistent, and the study of this method provides a basis for improving the fatigue reliability of freight car bodies.

Generally, the box bearing of railway freight cars has no bearing sample failure data at the end of the time-terminated reliability test. However, it is expensive and has high service reliability requirements. Given a small sample size and zero-failure data, the traditional failure probability calculation formula based on a large sample size and the reliability modeling technique cannot easily assess the reliability of rolling bearings accurately. Considering the applicability of the bearing of railway freight cars, this study integrated the prior information of samples and the simulation test information according to Bayes statistical theory, deduced the mathematical model of cumulative failure probability under failure-free data, calculated the distribution parameters using the least square method, and established the reliability estimation model of rolling bearings on the basis of Weibull distribution. The failure-free simulation data of rolling bearings were produced according to the Monte Carlo simulation, and the reliability of the journal bearing of railway freight cars was simulated and assessed by three methods. Simulation results demonstrate that the proposed reliable Bayes multilayer estimation method could not only meet the design requirements of the ISO 281 rolling bearing standards on that basis of the failure-free data and small sample size of the time-terminated simulation, but also assess the reliability of the rolling bearing of railway freight cars.