@article{SHAO2024, 
author = {Tao SHAO and Shuo WANG and Qinghua WANG and Tonghai WU and Zhifu HUANG},
title = {Comparison-embedded evidence-CNN model for fuzzy assessment of wear severity using multi-dimensional surface images},
year = {2024},
journal = {Friction},
volume = {12},
number = {6},
pages = {1098-1118},
keywords = {wear severity assessment, contrastive learning, subjective logic, Dempster–Shafer (D–S) evidence theory},
url = {https://www.sciopen.com/article/10.1007/s40544-023-0752-8},
doi = {10.1007/s40544-023-0752-8},
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.}
}