Research on Gear Surface Damage Recognition Based on Small Sample Deep Learning
Wang Xiaopeng
Hua Hongpeng
Lu Changqing
Peng Kun
Zhong Yuan
Wu Biqiong
Abstract：Gear surface damage is an important factor affecting gear transmission.It is extremely impor-tant to improve the efficiency and accuracy of gear surface damage identification.ResNet recognition model of gear surface damage is established based on Pytorch architecture,dataset is expanded by means of data enhance-ment,model training is optimized by means of transfer learning,and four ResNet structures are compared.The results show that the dataset composed of 640 images after the enhancement of 64 original image is not enough to meet the needs of model training for a large amount of data;using transfer learning can improve the speed and accuracy of model training,and meet the requirements of gear surface damage identification;the ResNet-101 model is the optimal structure in this framework.This research has important scientific significance and engi-neering value for the recognition of gear surface damage.
Keywords：Convolutional neural networkGear surface damageDeep learningTransfer learning
Publication Date：2024-04-15
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：6( 103-108 )