Rail defects can pose significant safety risks in railway operations, raising the need for effective detection methods. Acoustic Emission (AE) technology has shown promise for identifying and monitoring these defects,...Rail defects can pose significant safety risks in railway operations, raising the need for effective detection methods. Acoustic Emission (AE) technology has shown promise for identifying and monitoring these defects, and this study evaluates an advanced on-vehicle AE detection approach using bone-conduct sensors—a solution to improve upon previous AE methods of using on-rail sensor installations, which required extensive, costly on-rail sensor networks with limited effectiveness. In response to these challenges, the study specifically explored bone-conduct sensors mounted directly on the vehicle rather than rails by evaluating AE signals generated by the interaction between rails and the train’s wheels while in motion. In this research, a prototype detection system was developed and tested through initial trials at the Nevada Railroad Museum using a track with pre-damaged welding defects. Further testing was conducted at the Transportation Technology Center Inc. (rebranded as MxV Rail) in Colorado, where the system’s performance was evaluated across various defect types and train speeds. The results indicated that bone-conduct sensors were insufficient for detecting AE signals when mounted on moving vehicles. These findings highlight the limitations of contact-based methods in real-world applications and indicate the need for exploring improved, non-contact approaches.展开更多
Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operat...Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.展开更多
The feasibility of monitoring the dipped rail joint defects has been theoretically investigated by simulating a locomotive-mounted acceleration system negoti- ating several types of dipped rail defects. Initially, a c...The feasibility of monitoring the dipped rail joint defects has been theoretically investigated by simulating a locomotive-mounted acceleration system negoti- ating several types of dipped rail defects. Initially, a comprehensive locomotive-track model was developed using the multi-body dynamics approach. In this model, the locomotive car-body, bogie frames, wheelsets and driving motors are considered as rigid bodies; track modelling was also taken into account. A quantitative relationship between the characteristics （peak-peak values） of the axle box accelerations and the rail defects was determined through simulations. Therefore, the proposed approach, which combines defect analysis and comparisons with theoretical results, will enhance the ability for long-term monitoring and assessment of track systems and provides more informed preventative track maintenance strategies.展开更多
A more effective and accurate improved Sobel algorithm has been developed to detect surface defects on heavy rails. The proposed method can make up for the mere sensitivity to X and Y directions of the Sobel algorithm...A more effective and accurate improved Sobel algorithm has been developed to detect surface defects on heavy rails. The proposed method can make up for the mere sensitivity to X and Y directions of the Sobel algorithm by adding six templates at different directions. Meanwhile, an experimental platform for detecting surface defects consisting of the bed-jig, image-forming system with CCD cameras and light sources, parallel computer system and cable system has been constructed. The detection results of the backfin defects show that the improved Sobel algorithm can achieve an accurate and efficient positioning with decreasing interference noises to the defect edge. It can also extract more precise features and characteristic parameters of the backfin defect. Furthermore, the BP neural network adopted for defects classification with the inputting characteristic parameters of improved Sobel algorithm can obtain the optimal training precision of 0.0095827 with 106 iterative steps and time of 3 s less than Sobel algorithm with 146 steps and 5 s. Finally, an enhanced identification rate of 10% for the defects is also confirmed after the Sobel algorithm is improved.展开更多
To guarantee the safety of railway operations,the swift detection of rail surface defects becomes imperative.Traditional methods of manual inspection and conventional nondestructive testing prove inefficient,especiall...To guarantee the safety of railway operations,the swift detection of rail surface defects becomes imperative.Traditional methods of manual inspection and conventional nondestructive testing prove inefficient,especially when scaling to extensive railway networks.Moreover,the unpredictable and intricate nature of defect edge shapes further complicates detection efforts.Addressing these challenges,this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network(UPerNet)tailored for rail surface defect detection.Notably,the Swin Transformer Tiny version(Swin-T)network,underpinned by the Transformer architecture,is employed for adept feature extraction.This approach capitalizes on the global information present in the image and sidesteps the issue of inductive preference.The model’s efficiency is further amplified by the windowbased self-attention,which minimizes the model’s parameter count.We implement the cross-GPU synchronized batch normalization(SyncBN)for gradient optimization and integrate the Lovász-hinge loss function to leverage pixel dependency relationships.Experimental evaluations underscore the efficacy of our improved UPerNet,with results demonstrating Pixel Accuracy(PA)scores of 91.39%and 93.35%,Intersection over Union(IoU)values of 83.69%and 87.58%,Dice Coefficients of 91.12%and 93.38%,and Precision metrics of 90.85%and 93.41%across two distinct datasets.An increment in detection accuracy was discernible.For further practical applicability,we deploy semantic segmentation of rail surface defects,leveraging connected component processing techniques to distinguish varied defects within the same frame.By computing the actual defect length and area,our deep learning methodology presents results that offer intuitive insights for railway maintenance professionals.展开更多
On-line rail milling technologies have been applied in rail maintenance, and are proving to be efficient and environmental friendly. Based on the field data of on-line rail milling, a program for comparing rail transv...On-line rail milling technologies have been applied in rail maintenance, and are proving to be efficient and environmental friendly. Based on the field data of on-line rail milling, a program for comparing rail transverse profiles before and after milling was designed and the root mean square （RMS） amplitude of longitudinal profile was calculated. The application of on-line rail milling technology in removing rail surface defects, re-profiling railhead transverse profiles, smoothing longitudinal profiles and improving welding joint irregularity were analyzed. The results showed that the on-line rail milling technology can remove the surface defects at the rail crown and gauge comer perfectly, re-profile railhead transverse profile with a tolerance of - 1. 0-0.2 ram, improve longitudinal irregularity of rail surface, with the RMS amplitude of irregularity reduced more than 50% and the number of out-of- limited amplitude reduced by 42% - 82% in all wavelength ranges. The improvement of welding joint irregularity depends on the amount of metal removal determined by the milling equipment and the primal amplitude.展开更多
Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-ha...Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-haul trains make the rail surface susceptible to damage such as rail corrugation,spalling and abrasion,threatening operational safety.To address the issue,this paper proposes a multi-source data fusion method for identifying rail surface defects on heavy-haul railways.First,complete ensemble empirical mode decomposition with adaptive noise is used to decompose vibration signals and extract multi-dimensional vibration features.Next,dynamic time warping is applied to align rail profile data and extract key geometric features.Then,the vibration features and profile features are fused using Relief-F to select the most discriminative features.Finally,a support vector machine is utilized for defect identification.Experiment results show that the proposed method achieves high accuracy in identifying rail surface defects,with an accuracy of 96.4%.展开更多
文摘Rail defects can pose significant safety risks in railway operations, raising the need for effective detection methods. Acoustic Emission (AE) technology has shown promise for identifying and monitoring these defects, and this study evaluates an advanced on-vehicle AE detection approach using bone-conduct sensors—a solution to improve upon previous AE methods of using on-rail sensor installations, which required extensive, costly on-rail sensor networks with limited effectiveness. In response to these challenges, the study specifically explored bone-conduct sensors mounted directly on the vehicle rather than rails by evaluating AE signals generated by the interaction between rails and the train’s wheels while in motion. In this research, a prototype detection system was developed and tested through initial trials at the Nevada Railroad Museum using a track with pre-damaged welding defects. Further testing was conducted at the Transportation Technology Center Inc. (rebranded as MxV Rail) in Colorado, where the system’s performance was evaluated across various defect types and train speeds. The results indicated that bone-conduct sensors were insufficient for detecting AE signals when mounted on moving vehicles. These findings highlight the limitations of contact-based methods in real-world applications and indicate the need for exploring improved, non-contact approaches.
基金Supported by National Natural Science Foundation of China(Grant No.61573233)Guangdong Provincial Natural Science Foundation of China(Grant No.2021A1515010661)Guangdong Provincial Special Projects in Key Fields of Colleges and Universities of China(Grant No.2020ZDZX2005).
文摘Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains.For this reason,a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper.First,a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples.Second,an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image,which improved the accuracy of defect detection.Subsequently,to optimize the detection performance of the proposed model,the Mish activation function was used to design the block module of the feature extraction network.Finally,the pro-posed rail defect detection model was trained.The experimental results showed that the precision rate and F1score of the proposed method were as high as 98%,while the model’s recall rate reached 99%.Specifically,good detec-tion results were achieved for different types of defects,which provides a reference for the engineering application of internal defect detection.Experimental results verified the effectiveness of the proposed method.
基金the support of the Centre for Railway Engineering, Central Queensland Universitythe support from State Key Laboratory of Traction Power, Southwest Jiaotong University in the Open Projects: TPL1504, ‘Study on heavy haul train and coupler system dynamics’
文摘The feasibility of monitoring the dipped rail joint defects has been theoretically investigated by simulating a locomotive-mounted acceleration system negoti- ating several types of dipped rail defects. Initially, a comprehensive locomotive-track model was developed using the multi-body dynamics approach. In this model, the locomotive car-body, bogie frames, wheelsets and driving motors are considered as rigid bodies; track modelling was also taken into account. A quantitative relationship between the characteristics （peak-peak values） of the axle box accelerations and the rail defects was determined through simulations. Therefore, the proposed approach, which combines defect analysis and comparisons with theoretical results, will enhance the ability for long-term monitoring and assessment of track systems and provides more informed preventative track maintenance strategies.
基金Project(51174151)supported by the National Natural Science Foundation of ChinaProject(2010Z19003)supported by the Major Scientific Research Program of Hubei Provincial Department of Education,ChinaProject(2010CDB03403)supported by the Natural Science Foundation of Science and Technology Department of Hubei Province,China
文摘A more effective and accurate improved Sobel algorithm has been developed to detect surface defects on heavy rails. The proposed method can make up for the mere sensitivity to X and Y directions of the Sobel algorithm by adding six templates at different directions. Meanwhile, an experimental platform for detecting surface defects consisting of the bed-jig, image-forming system with CCD cameras and light sources, parallel computer system and cable system has been constructed. The detection results of the backfin defects show that the improved Sobel algorithm can achieve an accurate and efficient positioning with decreasing interference noises to the defect edge. It can also extract more precise features and characteristic parameters of the backfin defect. Furthermore, the BP neural network adopted for defects classification with the inputting characteristic parameters of improved Sobel algorithm can obtain the optimal training precision of 0.0095827 with 106 iterative steps and time of 3 s less than Sobel algorithm with 146 steps and 5 s. Finally, an enhanced identification rate of 10% for the defects is also confirmed after the Sobel algorithm is improved.
基金supported in part by the National Natural Science Foundation of China(Grant No.62066024)Gansu Province Higher Education Industry Support Plan(2021CYZC34)Lanzhou Talent Innovation and Entrepreneurship Project(2021-RC-27,2021-RC-45).
文摘To guarantee the safety of railway operations,the swift detection of rail surface defects becomes imperative.Traditional methods of manual inspection and conventional nondestructive testing prove inefficient,especially when scaling to extensive railway networks.Moreover,the unpredictable and intricate nature of defect edge shapes further complicates detection efforts.Addressing these challenges,this paper introduces an enhanced Unified Perceptual Parsing for Scene Understanding Network(UPerNet)tailored for rail surface defect detection.Notably,the Swin Transformer Tiny version(Swin-T)network,underpinned by the Transformer architecture,is employed for adept feature extraction.This approach capitalizes on the global information present in the image and sidesteps the issue of inductive preference.The model’s efficiency is further amplified by the windowbased self-attention,which minimizes the model’s parameter count.We implement the cross-GPU synchronized batch normalization(SyncBN)for gradient optimization and integrate the Lovász-hinge loss function to leverage pixel dependency relationships.Experimental evaluations underscore the efficacy of our improved UPerNet,with results demonstrating Pixel Accuracy(PA)scores of 91.39%and 93.35%,Intersection over Union(IoU)values of 83.69%and 87.58%,Dice Coefficients of 91.12%and 93.38%,and Precision metrics of 90.85%and 93.41%across two distinct datasets.An increment in detection accuracy was discernible.For further practical applicability,we deploy semantic segmentation of rail surface defects,leveraging connected component processing techniques to distinguish varied defects within the same frame.By computing the actual defect length and area,our deep learning methodology presents results that offer intuitive insights for railway maintenance professionals.
基金The National Natural Science Foundation of China(No.50908179)Specialized Research Fund for the Doctoral Program of Higher Education(No.200802471003)Program for Young Excellent Talents in Tongji University(No.2008KJ026)
文摘On-line rail milling technologies have been applied in rail maintenance, and are proving to be efficient and environmental friendly. Based on the field data of on-line rail milling, a program for comparing rail transverse profiles before and after milling was designed and the root mean square （RMS） amplitude of longitudinal profile was calculated. The application of on-line rail milling technology in removing rail surface defects, re-profiling railhead transverse profiles, smoothing longitudinal profiles and improving welding joint irregularity were analyzed. The results showed that the on-line rail milling technology can remove the surface defects at the rail crown and gauge comer perfectly, re-profile railhead transverse profile with a tolerance of - 1. 0-0.2 ram, improve longitudinal irregularity of rail surface, with the RMS amplitude of irregularity reduced more than 50% and the number of out-of- limited amplitude reduced by 42% - 82% in all wavelength ranges. The improvement of welding joint irregularity depends on the amount of metal removal determined by the milling equipment and the primal amplitude.
基金supported by the National Key R&D Program of China(No.2021YFF0501102)the National Natural Science Foundation of China(Grants No.52202392,U2368202,52372308,U2468203 and U2468206).
文摘Heavy-haul railways play a vital role in freight transportation,and the health of the rails directly impacts the safety and efficiency of railway operations.The heavy axle loads and long train compositions of heavy-haul trains make the rail surface susceptible to damage such as rail corrugation,spalling and abrasion,threatening operational safety.To address the issue,this paper proposes a multi-source data fusion method for identifying rail surface defects on heavy-haul railways.First,complete ensemble empirical mode decomposition with adaptive noise is used to decompose vibration signals and extract multi-dimensional vibration features.Next,dynamic time warping is applied to align rail profile data and extract key geometric features.Then,the vibration features and profile features are fused using Relief-F to select the most discriminative features.Finally,a support vector machine is utilized for defect identification.Experiment results show that the proposed method achieves high accuracy in identifying rail surface defects,with an accuracy of 96.4%.
