Defect detection of bearing side face based on sample data augmentation and convolutional neural network

Abstract

Bearing surface quality has significant impact on the working performance and durability of the mechanical transmission equipment. The traditional visual detection methods for bearing surface defects face the problems of weak versatility, low efficiency and poor reliability. In this paper, a deep learning detection method for bearing side face based on data augmentation and convolutional neural network is proposed. Firstly, image expansion based on circle detection and polar coordinate transformation is utilized to facilitate the labeling process and improve the significance of bearing defect area. Secondly, a bearing sample data augmentation method is designed to construct the defect data set. Semi-supervised data enhancement based on local defect features, improved RA strategy, and Mosaic algorithm are used to augment the initial bearing sample data set. Thirdly, an improved Faster R-CNN framework for bearing defect detection is established. The ROI align pooling is used to improve the continuity of output features. The Resnet101 network and Leaky Relu activation function are used to avoid the tiny defect feature loss and function dead zone. Furthermore, the FPN is integrated into Resnet101 to improve the detection precision for multi-scale bearing defects. Experimental results show that the proposed method can effectively achieve accurate and rapid defect detection of bearing surface, with a mAP of 98.18%. The proposed data augmentation strategy and defect detection framework show great application potential in the automatic surface detection of mechanical components.

CAC-YOLOv8: real-time bearing defect detection based on channel attenuation and expanded receptive field strategy

“…They introduced a bidirectional feature pyramid network for fast and efficient multiscale feature fusion and adopted the Focal loss EIoU as the loss function to improve the model's accuracy in locating crack boundaries. Liang et al [15] proposed a bearing defect detection method based on data augmentation and improved Faster R-CNN. They simplified annotations through circle detection and polar coordinate transformation, used semi-supervised data augmentation to construct a defect dataset, ultimately achieving highly efficient and accurate detection with a rate of 98.18%.…”

The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning

“…With the development of machine vision, some enterprises are opting to use industrial cameras instead of human eyes, employing defect detection algorithms to replace manual decision making [8]. Existing defect detection algorithms can be broadly categorized into traditional image processing methods [9] and deep learning-based object detection algorithms [10].…”

Research on Bearing Surface Scratch Detection Based on Improved YOLOV5

“…Tise method improves and combines the Ostu algorithm and the Canny algorithm to enhance the completeness and accuracy of bearing surface defect segmentation. Dan L et al [21] proposed a deep learning method for bearing face detection based on data enhancement and improved fast RCNN, using a semi-supervised data enhancement approach based on local flaw features, the improved RA strategy, and the mosaic algorithm to enhance the initial bearing sample data. This method can effectively achieve accurate and fast bearing face flaw detection.…”