Research On Defects Image Detection Techniques Of Freight Car’s Running Gear Springs

As the key component of the freight car’s bogie, running gear springs are easily fractured and lost because of the materials defects and abnormal impact, which will lose the role of buffering and damping vibration and threaten freight car’s security.Since always, detection of the springs defects was mainly accomplished by manual work. Not only the efficiency was low, the reliability was poor, but also the labor intensity was big, and there’s a security risk. The trouble of moving freight car detection system could detect the springs defects by the way of man-machine integration, which, to some extent, reduced the labor intensity and improved the defects detection rate. But in fact, the system itself only finished image collection, transmission and some pretreatment, defects recognition was still completed by worker’s naked eye. So there were still many problems and needed to be further improved and upgraded. Consequently, this thesis studied how to automatically identify the springs defects through using image processing techniques in order to improve the defects detection rate and guarantee the freight car’s security.Firstly, on the premise of investigating the research status of automatic recognition of springs defects based on image processing techniques at home and abroad, springs location algorithm was studied in this thesis. It was first to correct the uneven illumination and take binaryzation on image. Then the springs’boundary was found by binary image scanning and features analysis. Through experiment on120images, the results showed that the algorithm was appropriate for springs location of different models and different illumination environment, and the accuracy rate was above95%.Secondly, according to the structure features of spring itself, a spring loss recognition algorithm was proposed. After springs location, division of single spring was operated, then one could judge whether there was spring loss by comparison of the adjacent spring width.Finally, spring fracture recognition algorithm based on pattern recognition theory was studied in this thesis. Gray level co-occurrence matrix was used to extract the image texture feature; Relief algorithm was used to choose the optimal feature subset of image; minimum distance of template matching was used in image recognition. Through experiment on fracture springs images which were simulated, the judgment accuracy rate was above90%.This study provides algorithm reference for the realization of springs defects automatic recognition with high accuracy and good commonality and has a good application prospect.