Comparison-embedded evidence-CNN model for fuzzy assessment of wear severity using multi-dimensional surface images Article Swipe
Tao Shao
,
Shuo Wang
,
Qinghua Wang
,
Tonghai Wu
,
Zhifu Huang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s40544-023-0752-8
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40544-023-0752-8
- https://link.springer.com/content/pdf/10.1007/s40544-023-0752-8.pdf
- OA Status
- diamond
- Cited By
- 5
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389227397
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389227397Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s40544-023-0752-8Digital Object Identifier
- Title
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Comparison-embedded evidence-CNN model for fuzzy assessment of wear severity using multi-dimensional surface imagesWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-01Full publication date if available
- Authors
-
Tao Shao, Shuo Wang, Qinghua Wang, Tonghai Wu, Zhifu HuangList of authors in order
- Landing page
-
https://doi.org/10.1007/s40544-023-0752-8Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s40544-023-0752-8.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s40544-023-0752-8.pdfDirect OA link when available
- Concepts
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Artificial intelligence, Fuzzy logic, Computer science, Credibility, Measurement uncertainty, Surface (topology), Machine learning, Mathematics, Statistics, Geometry, Political science, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2025: 4, 2024: 1Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2948164205, https://openalex.org/W2888205466, https://openalex.org/W4283784399, https://openalex.org/W3195497677, https://openalex.org/W4225265862, https://openalex.org/W2035912441, https://openalex.org/W3041485797, https://openalex.org/W2898930659, https://openalex.org/W3174106600, https://openalex.org/W3034922411, https://openalex.org/W3120655525, https://openalex.org/W3005590642, https://openalex.org/W4206260104, https://openalex.org/W3209637765, https://openalex.org/W3004904155, https://openalex.org/W2962414238, https://openalex.org/W3132322119, https://openalex.org/W3154138058, https://openalex.org/W2062639947, https://openalex.org/W2133531916, https://openalex.org/W2940116336, https://openalex.org/W2947827250, https://openalex.org/W2937549716, https://openalex.org/W2906256948, https://openalex.org/W2919115771, https://openalex.org/W2317595875, https://openalex.org/W3007823438, https://openalex.org/W2907348177, https://openalex.org/W4293635930, https://openalex.org/W2793668851, https://openalex.org/W4303968625, https://openalex.org/W2063499201, https://openalex.org/W3005806855, https://openalex.org/W2811513716 |
| referenced_works_count | 34 |
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