Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition Monitoring Article Swipe
Wei Sun
,
Jie Zhou
,
Bintao Sun
,
Yuqing Zhou
,
Yongying Jiang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/mi13060873
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/mi13060873
Tool condition monitoring (TCM) is of great importance for improving the manufacturing efficiency and surface quality of workpieces. Data-driven machine learning methods are widely used in TCM and have achieved many good results. However, in actual industrial scenes, labeled data are not available in time in the target domain that significantly affect the performance of data-driven methods. To overcome this problem, a new TCM method combining the Markov transition field (MTF) and the deep domain adaptation network (DDAN) is proposed. A few vibration signals collected in the TCM experiments were represented in 2D images through MTF to enrich the features of the raw signals. The transferred ResNet50 was used to extract deep features of these 2D images. DDAN was employed to extract deep domain-invariant features between the source and target domains, in which the maximum mean discrepancy (MMD) is applied to measure the distance between two different distributions. TCM experiments show that the proposed method significantly outperforms the other three benchmark methods and is more robust under varying working conditions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/mi13060873
- https://www.mdpi.com/2072-666X/13/6/873/pdf?version=1654156974
- OA Status
- gold
- Cited By
- 19
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281755338
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281755338Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/mi13060873Digital Object Identifier
- Title
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Markov Transition Field Enhanced Deep Domain Adaptation Network for Milling Tool Condition MonitoringWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-05-31Full publication date if available
- Authors
-
Wei Sun, Jie Zhou, Bintao Sun, Yuqing Zhou, Yongying JiangList of authors in order
- Landing page
-
https://doi.org/10.3390/mi13060873Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-666X/13/6/873/pdf?version=1654156974Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2072-666X/13/6/873/pdf?version=1654156974Direct OA link when available
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Computer science, Artificial intelligence, Benchmark (surveying), Deep learning, Domain (mathematical analysis), Field (mathematics), Hidden Markov model, Pattern recognition (psychology), Domain adaptation, Mathematics, Geodesy, Geography, Mathematical analysis, Pure mathematics, Classifier (UML)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 4, 2023: 8Per-year citation counts (last 5 years)
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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