Abstract

Wear topography is a significant indicator of tribological behavior for the inspection of machine health conditions. An intelligent in-suit wear assessment method for random topography is here proposed. Three-dimension (3D) topography is employed to address the uncertainties in wear evaluation. Initially, 3D topography reconstruction from a worn surface is accomplished with photometric stereo vision (PSV). Then, the wear features are identified by a contrastive learning-based extraction network (WSFE-Net) including the relative and temporal prior knowledge of wear mechanisms. Furthermore, the typical wear degrees including mild, moderate, and severe are evaluated by a wear severity assessment network (WSA-Net) for the probability and its associated uncertainty based on subjective logic. By integrating the evidence information from 2D and 3D-damage surfaces with Dempster–Shafer (D–S) evidence, the uncertainty of severity assessment results is further reduced. The proposed model could constrain the uncertainty below 0.066 in the wear degree evaluation of a continuous wear experiment, which reflects the high credibility of the evaluation result.

中文翻译：

---

使用多维表面图像模糊评估磨损严重程度的比较嵌入式证据 CNN 模型

摘要

磨损形貌是检查机器健康状况的摩擦学行为的重要指标。本文提出了一种针对随机地形的智能西装穿着评估方法。采用三维 (3D) 形貌来解决磨损评估中的不确定性。最初，磨损表面的 3D 形貌重建是通过光度立体视觉 (PSV) 完成的。然后，通过基于对比学习的提取网络（WSFE-Net）识别磨损特征，包括磨损机制的相对和时间先验知识。此外，磨损严重程度评估网络（WSA-Net）基于主观逻辑评估典型磨损程度（包括轻度、中度和重度）的概率及其相关的不确定性。通过将 2D 和 3D 损伤表面的证据信息与 Dempster–Shafer (D–S) 证据相结合，严重性评估结果的不确定性进一步降低。该模型能够将连续磨损实验磨损程度评估的不确定度限制在0.066以下，体现了评估结果的较高可信度。