Multi-feature spatial distribution alignment enhanced domain adaptive method for tool condition monitoring Article Swipe
Z. K. Hei
,
Bintao Sun
,
Gaonghai Wang
,
Yongjian Lou
,
Yuqing Zhou
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.17531/ein/171750
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.17531/ein/171750
Transfer learning (TL) has been successfully implemented in tool condition monitoring (TCM) to address the lack of labeled data in real industrial scenarios. In current TL models, the domain offset in the joint distribution of input feature and output label still exists after the feature distribution of the two domains is aligned, resulting in performance degradation. A multiple feature spatial distribution alignment (MSDA) method is proposed, Including Correlation alignment for deep domain adaptation (Deep CORAL) and Joint maximum mean difference (JMMD). Deep CORAL is employed to learn nonlinear transformations, align source and target domains at the feature level through the second-order statistical correlations. JMMD is applied to improve domain alignment by aligning the joint distribution of input features and output labels. ResNet18 combining with bidirectional short-term memory network and attention mechanism is developed to extract the invariant features. TCM experiments with four transfer tasks were conducted and demonstrated the effectiveness of the proposed method.
Related Topics
Concepts
Artificial intelligence
Computer science
Transfer of learning
Feature (linguistics)
Pattern recognition (psychology)
Domain adaptation
Joint probability distribution
Offset (computer science)
Domain (mathematical analysis)
Mathematics
Classifier (UML)
Statistics
Linguistics
Mathematical analysis
Philosophy
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17531/ein/171750
- https://ein.org.pl/pdf-171750-94118?filename=Multi_feature spatial.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386444463
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386444463Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.17531/ein/171750Digital Object Identifier
- Title
-
Multi-feature spatial distribution alignment enhanced domain adaptive method for tool condition monitoringWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-05Full publication date if available
- Authors
-
Z. K. Hei, Bintao Sun, Gaonghai Wang, Yongjian Lou, Yuqing ZhouList of authors in order
- Landing page
-
https://doi.org/10.17531/ein/171750Publisher landing page
- PDF URL
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https://ein.org.pl/pdf-171750-94118?filename=Multi_feature spatial.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://ein.org.pl/pdf-171750-94118?filename=Multi_feature spatial.pdfDirect OA link when available
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Artificial intelligence, Computer science, Transfer of learning, Feature (linguistics), Pattern recognition (psychology), Domain adaptation, Joint probability distribution, Offset (computer science), Domain (mathematical analysis), Mathematics, Classifier (UML), Statistics, Linguistics, Mathematical analysis, Philosophy, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2896921584, https://openalex.org/W2805797750, https://openalex.org/W3095534907, https://openalex.org/W4200318892, https://openalex.org/W2077341607, https://openalex.org/W2146842127, https://openalex.org/W2312372500, https://openalex.org/W2110097068, https://openalex.org/W2763989623, https://openalex.org/W4311715044, https://openalex.org/W4241570161, https://openalex.org/W2194775991, https://openalex.org/W3060850527, https://openalex.org/W3013107038, https://openalex.org/W4303712171, https://openalex.org/W2344695726, https://openalex.org/W2940750478, https://openalex.org/W3148571989, https://openalex.org/W3157539115, https://openalex.org/W3082187180, https://openalex.org/W3015913963, https://openalex.org/W2938021361, https://openalex.org/W3146674346, https://openalex.org/W4323671053, https://openalex.org/W4315784642, https://openalex.org/W1982291194, https://openalex.org/W2963275094, https://openalex.org/W3003767554, https://openalex.org/W3201589928, https://openalex.org/W4285131832, https://openalex.org/W3207672850, https://openalex.org/W3194833849, https://openalex.org/W3043899764, https://openalex.org/W4317771505, https://openalex.org/W4200385936, https://openalex.org/W4297973882, https://openalex.org/W3157618166, https://openalex.org/W2572421041, https://openalex.org/W2159291411, https://openalex.org/W1882958252, https://openalex.org/W2765528441, https://openalex.org/W2964278684, https://openalex.org/W2104094955 |
| referenced_works_count | 43 |
| abstract_inverted_index.A | 56 |
| abstract_inverted_index.In | 23 |
| abstract_inverted_index.TL | 25 |
| abstract_inverted_index.at | 94 |
| abstract_inverted_index.by | 110 |
| abstract_inverted_index.in | 7, 19, 30, 53 |
| abstract_inverted_index.is | 50, 64, 83, 104, 131 |
| abstract_inverted_index.of | 16, 34, 46, 115, 150 |
| abstract_inverted_index.to | 12, 85, 106, 133 |
| abstract_inverted_index.TCM | 138 |
| abstract_inverted_index.and | 37, 75, 91, 118, 128, 146 |
| abstract_inverted_index.for | 69 |
| abstract_inverted_index.has | 3 |
| abstract_inverted_index.the | 14, 27, 31, 43, 47, 95, 99, 112, 135, 148, 151 |
| abstract_inverted_index.two | 48 |
| abstract_inverted_index.(TL) | 2 |
| abstract_inverted_index.Deep | 81 |
| abstract_inverted_index.JMMD | 103 |
| abstract_inverted_index.been | 4 |
| abstract_inverted_index.data | 18 |
| abstract_inverted_index.deep | 70 |
| abstract_inverted_index.four | 141 |
| abstract_inverted_index.lack | 15 |
| abstract_inverted_index.mean | 78 |
| abstract_inverted_index.real | 20 |
| abstract_inverted_index.tool | 8 |
| abstract_inverted_index.were | 144 |
| abstract_inverted_index.with | 123, 140 |
| abstract_inverted_index.(Deep | 73 |
| abstract_inverted_index.(TCM) | 11 |
| abstract_inverted_index.CORAL | 82 |
| abstract_inverted_index.Joint | 76 |
| abstract_inverted_index.after | 42 |
| abstract_inverted_index.align | 89 |
| abstract_inverted_index.input | 35, 116 |
| abstract_inverted_index.joint | 32, 113 |
| abstract_inverted_index.label | 39 |
| abstract_inverted_index.learn | 86 |
| abstract_inverted_index.level | 97 |
| abstract_inverted_index.still | 40 |
| abstract_inverted_index.tasks | 143 |
| abstract_inverted_index.(MSDA) | 62 |
| abstract_inverted_index.CORAL) | 74 |
| abstract_inverted_index.domain | 28, 71, 108 |
| abstract_inverted_index.exists | 41 |
| abstract_inverted_index.memory | 126 |
| abstract_inverted_index.method | 63 |
| abstract_inverted_index.offset | 29 |
| abstract_inverted_index.output | 38, 119 |
| abstract_inverted_index.source | 90 |
| abstract_inverted_index.target | 92 |
| abstract_inverted_index.(JMMD). | 80 |
| abstract_inverted_index.address | 13 |
| abstract_inverted_index.applied | 105 |
| abstract_inverted_index.current | 24 |
| abstract_inverted_index.domains | 49, 93 |
| abstract_inverted_index.extract | 134 |
| abstract_inverted_index.feature | 36, 44, 58, 96 |
| abstract_inverted_index.improve | 107 |
| abstract_inverted_index.labeled | 17 |
| abstract_inverted_index.labels. | 120 |
| abstract_inverted_index.maximum | 77 |
| abstract_inverted_index.method. | 153 |
| abstract_inverted_index.models, | 26 |
| abstract_inverted_index.network | 127 |
| abstract_inverted_index.spatial | 59 |
| abstract_inverted_index.through | 98 |
| abstract_inverted_index.ResNet18 | 121 |
| abstract_inverted_index.Transfer | 0 |
| abstract_inverted_index.aligned, | 51 |
| abstract_inverted_index.aligning | 111 |
| abstract_inverted_index.employed | 84 |
| abstract_inverted_index.features | 117 |
| abstract_inverted_index.learning | 1 |
| abstract_inverted_index.multiple | 57 |
| abstract_inverted_index.proposed | 152 |
| abstract_inverted_index.transfer | 142 |
| abstract_inverted_index.Including | 66 |
| abstract_inverted_index.alignment | 61, 68, 109 |
| abstract_inverted_index.attention | 129 |
| abstract_inverted_index.combining | 122 |
| abstract_inverted_index.condition | 9 |
| abstract_inverted_index.conducted | 145 |
| abstract_inverted_index.developed | 132 |
| abstract_inverted_index.features. | 137 |
| abstract_inverted_index.invariant | 136 |
| abstract_inverted_index.mechanism | 130 |
| abstract_inverted_index.nonlinear | 87 |
| abstract_inverted_index.proposed, | 65 |
| abstract_inverted_index.resulting | 52 |
| abstract_inverted_index.adaptation | 72 |
| abstract_inverted_index.difference | 79 |
| abstract_inverted_index.industrial | 21 |
| abstract_inverted_index.monitoring | 10 |
| abstract_inverted_index.scenarios. | 22 |
| abstract_inverted_index.short-term | 125 |
| abstract_inverted_index.Correlation | 67 |
| abstract_inverted_index.experiments | 139 |
| abstract_inverted_index.implemented | 6 |
| abstract_inverted_index.performance | 54 |
| abstract_inverted_index.statistical | 101 |
| abstract_inverted_index.degradation. | 55 |
| abstract_inverted_index.demonstrated | 147 |
| abstract_inverted_index.distribution | 33, 45, 60, 114 |
| abstract_inverted_index.second-order | 100 |
| abstract_inverted_index.successfully | 5 |
| abstract_inverted_index.bidirectional | 124 |
| abstract_inverted_index.correlations. | 102 |
| abstract_inverted_index.effectiveness | 149 |
| abstract_inverted_index.transformations, | 88 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.69413526 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |