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

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外文摘要：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.

外文关键词：

wear severity assessmentcontrastive learningsubjective logicDempster-Shafer(D-S)evidence theory

作者：

Tao SHAO、Shuo WANG、Qinghua WANG、Tonghai WU、Zhifu HUANG

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作者单位：

Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System,Xi'an Jiaotong University,Xi'an 710049,China

State Key Laboratory for Mechanical Behavior of Materials,Xi'an Jiaotong University,Xi'an 710049,China

基金：

国家自然科学基金国家自然科学基金中国博士后科学基金Open Foundation of State Key Laboratory of Compressor Technology(Compressor Technology Laboratory of Anhui Province)

项目编号：

52105159519754552021M702594SKL-YSJ202102

出版年：

2024

DOI：

10.1007/s40544-023-0752-8

摩擦(英文)

CSTPCDEI

ISSN：2223-7690

年,卷(期)：2024.12(6)

参考文献量36