Gear Pitting Measurement by Multi-Scale Splicing Attention U-Net

Abstract

The judgment of gear failure is based on the pitting area ratio of gear. Traditional gear pitting calculation method mainly rely on manual visual inspection. This method is greatly affected by human factors, and is greatly affected by the working experience, training degree and fatigue degree of the detection personnel, so the detection results may be biased. The non-contact computer vision measurement can carry out non-destructive testing and monitoring under the working condition of the machine, and has high detection accuracy. To improve the measurement accuracy of gear pitting, a novel multi-scale splicing attention U-Net (MSSA U-Net) is explored in this study. An image splicing module is first proposed for concatenating the output feature maps of multiple convolutional layers into a splicing feature map with more semantic information. Then, an attention module is applied to select the key features of the splicing feature map. Given that MSSA U-Net adequately uses multi-scale semantic features, it has better segmentation performance on irregular small objects than U-Net and attention U-Net. On the basis of the designed visual detection platform and MSSA U-Net, a methodology for measuring the area ratio of gear pitting is proposed. With three datasets, experimental results show that MSSA U-Net is superior to existing typical image segmentation methods and can accurately segment different levels of pitting due to its strong segmentation ability. Therefore, the proposed methodology can be effectively applied in measuring the pitting area ratio and determining the level of gear pitting.