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
The prediction of the wheel wear is a fundamental problem in heavy haul railway. A numerical methodology is introduced to simulate the wheel wear evolution of heavy haul freight car. The methodology includes the spati...The prediction of the wheel wear is a fundamental problem in heavy haul railway. A numerical methodology is introduced to simulate the wheel wear evolution of heavy haul freight car. The methodology includes the spatial coupling dynamics of vehicle and track, the three-dimensional rolling contact analysis of wheel-rail, the Specht's material wear model, and the strategy for reproducing the actual operation conditions of railway. The freight vehicle is treated as a full 3D rigid multi-body model. Every component is built detailedly and various contact interactions between parts are accurately simulated, taking into account the real clearances. The wheel-rail rolling contact calculation is carried out based on Hertz's theory and Kalker's FASTSIM algorithm. The track model is built based on field measurements. The material loss due to wear is evaluated according to the Specht's model in which the wear coefficient varies with the wear intensity. In order to exactly reproduce the actual operating conditions of railway,dynamic simulations are performed separately for all possible track conditions and running velocities in each iterative step.Dimensionless weight coefficients are introduced that determine the ratios of different cases and are obtained through site survey. For the wheel profile updating, an adaptive step strategy based on the wear depth is introduced, which can effectively improve the reliability and stability of numerical calculation. At last, the wear evolution laws are studied by the numerical model for different wheels of heavy haul freight vehicle running in curves. The results show that the wear of the front wheelset is more serious than that of the rear wheelset for one bogie, and the difference is more obvious for the outer wheels. The wear of the outer wheels is severer than that of the inner wheels. The wear of outer wheels mainly distributes near the flange and the root; while the wear of inner wheels mainly distributes around the nominal rolling circle. For the outer wheel of front wheelset o展开更多
The U75V 60 kg/m heavy rail samples were heated to 900 ℃ in a resistance furnace for a fixed duration of 50 min. Under this condition, the samples were austenitized totally. Then, the samples were dragged out of furn...The U75V 60 kg/m heavy rail samples were heated to 900 ℃ in a resistance furnace for a fixed duration of 50 min. Under this condition, the samples were austenitized totally. Then, the samples were dragged out of furnace and cooled for 25 s in the open air. After that, the samples entered into the air spraying channel, and were cooled from the top and both sides by compressed air. During this period, main technical parameters were changed such as the distance between spray nozzles and surface of rail head, air pressure, air spraying time and air temperature. So under laboratory condition, optimal heat-treating parameters of U75V 60 kg/m heavy rail were determined as the distance between spray nozzles and surface of rail head of 15 mm, air pressure of 0.26 MPa, cooling time of 80 s, and air temperature of 28 ℃. The surface temperature at different positions of heavy rail was measured before and after heat treatment, and the temperature changing law was determined. The self tempering occurred on the surface of rail head after heat treatment, and the tempering temperature became the largest （about 3 min） after heat treatment, separately 528, 524 and 536 ℃ at the center, top fillet and bottom fillet of rail head. The heavy rail was cooled in open air after heat treatment; during this period, the temperature gap on the surface of heavy rail became smaller and smaller, and was reduced to zero when being cooled for 20 min.展开更多
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Project(U1234211)supported of the National Natural Science Foundation of ChinaProject(20120009110020)supported by the Specialized Research Fund for Ph.D. Programs of Foundation of Ministry of Education of ChinaProject(SHGF-11-32)supported the Scientific and Technological Innovation Project of China Shenhua Energy Company Limited
文摘The prediction of the wheel wear is a fundamental problem in heavy haul railway. A numerical methodology is introduced to simulate the wheel wear evolution of heavy haul freight car. The methodology includes the spatial coupling dynamics of vehicle and track, the three-dimensional rolling contact analysis of wheel-rail, the Specht's material wear model, and the strategy for reproducing the actual operation conditions of railway. The freight vehicle is treated as a full 3D rigid multi-body model. Every component is built detailedly and various contact interactions between parts are accurately simulated, taking into account the real clearances. The wheel-rail rolling contact calculation is carried out based on Hertz's theory and Kalker's FASTSIM algorithm. The track model is built based on field measurements. The material loss due to wear is evaluated according to the Specht's model in which the wear coefficient varies with the wear intensity. In order to exactly reproduce the actual operating conditions of railway,dynamic simulations are performed separately for all possible track conditions and running velocities in each iterative step.Dimensionless weight coefficients are introduced that determine the ratios of different cases and are obtained through site survey. For the wheel profile updating, an adaptive step strategy based on the wear depth is introduced, which can effectively improve the reliability and stability of numerical calculation. At last, the wear evolution laws are studied by the numerical model for different wheels of heavy haul freight vehicle running in curves. The results show that the wear of the front wheelset is more serious than that of the rear wheelset for one bogie, and the difference is more obvious for the outer wheels. The wear of the outer wheels is severer than that of the inner wheels. The wear of outer wheels mainly distributes near the flange and the root; while the wear of inner wheels mainly distributes around the nominal rolling circle. For the outer wheel of front wheelset o
文摘The U75V 60 kg/m heavy rail samples were heated to 900 ℃ in a resistance furnace for a fixed duration of 50 min. Under this condition, the samples were austenitized totally. Then, the samples were dragged out of furnace and cooled for 25 s in the open air. After that, the samples entered into the air spraying channel, and were cooled from the top and both sides by compressed air. During this period, main technical parameters were changed such as the distance between spray nozzles and surface of rail head, air pressure, air spraying time and air temperature. So under laboratory condition, optimal heat-treating parameters of U75V 60 kg/m heavy rail were determined as the distance between spray nozzles and surface of rail head of 15 mm, air pressure of 0.26 MPa, cooling time of 80 s, and air temperature of 28 ℃. The surface temperature at different positions of heavy rail was measured before and after heat treatment, and the temperature changing law was determined. The self tempering occurred on the surface of rail head after heat treatment, and the tempering temperature became the largest （about 3 min） after heat treatment, separately 528, 524 and 536 ℃ at the center, top fillet and bottom fillet of rail head. The heavy rail was cooled in open air after heat treatment; during this period, the temperature gap on the surface of heavy rail became smaller and smaller, and was reduced to zero when being cooled for 20 min.
