基于规范化样本拆分的轴承缺陷检测

表面缺陷对轴承的性能和寿命存在严重影响.近年来,深度学习在缺陷检测中发挥了重要的作用,然而对于轴承检测而言,缺陷样本的采集耗时耗力.选择轴承内径作为研究对象,根据轴承的对称性特性提出一种规范化样本拆分方法,可有效扩充轴承样本数据集.分别采用不同的样本处理方法,而后利用ResNet网络训练轴承缺陷检测模型,进行多组对比实...

Bearing defects detection based on standardized sample split

Surface defects seriously affect the quality and service life of bearings. In recent years, deep learning has played an important role in defects detection, but for bearing detection, the collection of defects samples is time-consuming and labor-consuming. The bearing inner diameter was chosen as the detection object, a method of standardized sample split based on the symmetry of bearing was proposed, which could greatly increase the number of samples. Different sample processing methods were used respectively, and then the bearing defects detection model was trained by ResNet network to carry out several comparative experiments. The experimental results show that the detection effect is worse when the original images are directly used for training, and the area under the curve (AUC) of the model is only 0.558 0; after the samples are split, the trained model detection effect is better, and the model AUC is improved to 0.632 6; after the samples are corrected by four point perspective transformation, the detection effect is better, and the model AUC is increased to 0.661 3; after the original images are corrected by perspective transformation and the standardized samples are split, the detection effect is the best, and the model AUC is increased to 0.849 6.