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

作　　者：Tao SHAO Shuo WANG Qinghua WANG Tonghai WU Zhifu HUANG 

机构地区：[1]Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System,Xi’an Jiaotong University,Xi’an 710049,China [2]State Key Laboratory for Mechanical Behavior of Materials,Xi’an Jiaotong University,Xi’an 710049,China

出　　处：《Friction》2024年第6期1098-1118,共21页摩擦（英文版）

摘　　要：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.

关 键 词：wear severity assessment contrastive learning subjective logic Dempster-Shafer(D-S)evidence theory 

分 类 号：TH117.1[机械工程—机械设计及理论]