Comparison-embedded evidence-CNN model for fuzzy assessment of wear severity using multi-dimensional surface images

• Published in: Friction Vol. 12; no. 6; pp. 1098 - 1118
• Main Authors: Shao, Tao, Wang, Shuo, Wang, Qinghua, Wu, Tonghai, Huang, Zhifu
• Format: Journal Article
• Language: English
• Published: Beijing Tsinghua University Press 01.06.2024; Springer Nature B.V; SpringerOpen
• Subjects: contrastive learning; Corrosion and Coatings; Dempster–Shafer (D–S) evidence theory; Engineering; Fuzzy logic; Image reconstruction; Mechanical Engineering; Nanotechnology; Physical Chemistry; Research Article; subjective logic; Surfaces and Interfaces; Thin Films; Topography; Tribology; Uncertainty; Wear mechanisms; wear severity assessment; wear severity assessment; contrastive learning; Dempster–Shafer (D–S) evidence theory; subjective logic
• ISSN: 2223-7690; 2223-7704
• DOI: 10.1007/s40544-023-0752-8

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.