Automated assessment of gear wear mechanism and severity using mould images and convolutional neural networks

摘要

A novel methodology for automated wear mechanism and severity assessment combining surface replication, imaging and deep learning is proposed. A large dataset of images of gear teeth moulds was built and covers abrasive wear, macropitting and scuffing, and three severity levels for each mechanism, i.e., mild, moderate and severe. A two-level inference methodology was implemented, based on a first convolutional neural network (CNN), which contains multiple convolutional layers and is commonly used for image classification, for wear mechanism identification, followed by three CNNs for wear severity estimation. The first level obtained a test classification accuracy of 98.22% and the second of 95.16% on average. The two-level system was also applied to full tooth flank mould images to generate wear mechanism and severity maps showing the geographical distribution of wear.