Machine vision-based surface defect detection method for rolling bearings of metro vehicle

As the core component of the subway running gear, the safety and reliability of the bearing has been widely concerned. Under the influence of high speed, heavy load, long routing, sand erosion, rain corrosion and other operating conditions, the service conditions of bearings become very harsh, and damage inevitably occurs. At present, most of the nondestructive testing technologies used in metro bearings are black magnetic particle testing technology, which has the advantages of high sensitivity, high reliability and easy identification of defects. It is one of the most effective technologies widely recognized for the effective inspection of the surface or near-surface of ferromagnetic materials. To detect the surface defects of rolling bearings of metro vehicles, the images of black magnetic particle flaw detection defects of metro vehicles' rolling bearings are collected on the spot. By using image processing technology, all binary images of surface defects are extracted, and a data set including the original image and binary images of surface defects is established. It is pointed out that there are great difficulties and limitations in using traditional machine vision technology to identify subway bearing flaw detection defects. Thus, convolutional neural network is chosen to identify bearing defects to realize deep feature fusion. Through the comparison with several models, it is concluded that there is a certain improvement in the effect and accuracy of surface defect identification in this paper, which can not only reduce the labor intensity and training costs of personnel, but also ensure the efficiency and accuracy of bearing defect detection identification. It is the trend and hot spot of future development.