Abstract 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.

中文翻译：

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使用模具图像和卷积神经网络自动评估齿轮磨损机制和严重程度

摘要 提出了一种结合表面复制、成像和深度学习的自动磨损机制和严重性评估的新方法。建立了齿轮齿模具图像的大型数据集，涵盖磨料磨损、大点蚀和划伤，以及每种机制的三个严重程度，即轻度、中度和重度。基于第一个卷积神经网络 (CNN) 实施了两级推理方法，该网络包含多个卷积层，通常用于图像分类、磨损机制识别，然后是三个用于磨损严重程度估计的 CNN。第一级的测试分类准确率为 98.22%，第二级平均为 95.16%。