Wear state identification of ball screw meta action unit based on parameter optimization VMD and improved Bilstm Article Swipe
Hongyu Ge
,
Cangfu Wang
,
Anxiang Guo
,
Chuanwei Zhang
,
Zhan Zhao
,
Manzhi Yang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.17531/ein/191461
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.17531/ein/191461
In this paper, a novel neural network based on parameter optimization Variational Mode Decomposition (VMD) and improved bidirectional long short-term memory (BiLSTM) is proposed. Wear state recognition method of ball screw element action unit component based on Bilstm. Firstly, Tent chaotic map and adaptive sine cosine Algorithm were used to improve the improved Northern Goshawk Optimisation Algorithm (INGO) to verify the superiority of INGO algorithm and determine the optimal parameter combination of VMD. Secondly, INGO-VMD was used to decompose the collected vibration signals and calculate the correlation of IMF components, and the multi-feature information matrix that could characterize the wear state change of the lead screw was constructed after retaining the IMF components with large correlation. Finally, the divided feature information matrix and labels were input into the Bilstm network model of Bayesian optimization (BO) for training, and the Softmax classifier was used to classify and identify the wear state category.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17531/ein/191461
- OA Status
- gold
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- 1
- References
- 39
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400928806Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.17531/ein/191461Digital Object Identifier
- Title
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Wear state identification of ball screw meta action unit based on parameter optimization VMD and improved BilstmWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-07-22Full publication date if available
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Hongyu Ge, Cangfu Wang, Anxiang Guo, Chuanwei Zhang, Zhan Zhao, Manzhi YangList of authors in order
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https://doi.org/10.17531/ein/191461Publisher landing page
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goldOpen access status per OpenAlex
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- Concepts
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Ball screw, Identification (biology), Unit (ring theory), Computer science, Mathematics, Engineering, Mechanical engineering, Biology, Botany, Mathematics education, NutTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-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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| abstract_inverted_index.Wear | 24 |
| abstract_inverted_index.ball | 29 |
| abstract_inverted_index.into | 126 |
| abstract_inverted_index.lead | 104 |
| abstract_inverted_index.long | 18 |
| abstract_inverted_index.sine | 44 |
| abstract_inverted_index.that | 95 |
| abstract_inverted_index.this | 1 |
| abstract_inverted_index.unit | 33 |
| abstract_inverted_index.used | 48, 76, 142 |
| abstract_inverted_index.wear | 99, 148 |
| abstract_inverted_index.were | 47, 124 |
| abstract_inverted_index.with | 113 |
| abstract_inverted_index.(VMD) | 14 |
| abstract_inverted_index.after | 108 |
| abstract_inverted_index.based | 7, 35 |
| abstract_inverted_index.could | 96 |
| abstract_inverted_index.input | 125 |
| abstract_inverted_index.large | 114 |
| abstract_inverted_index.model | 130 |
| abstract_inverted_index.novel | 4 |
| abstract_inverted_index.screw | 30, 105 |
| abstract_inverted_index.state | 25, 100, 149 |
| abstract_inverted_index.(INGO) | 57 |
| abstract_inverted_index.Bilstm | 128 |
| abstract_inverted_index.action | 32 |
| abstract_inverted_index.change | 101 |
| abstract_inverted_index.cosine | 45 |
| abstract_inverted_index.labels | 123 |
| abstract_inverted_index.matrix | 94, 121 |
| abstract_inverted_index.memory | 20 |
| abstract_inverted_index.method | 27 |
| abstract_inverted_index.neural | 5 |
| abstract_inverted_index.paper, | 2 |
| abstract_inverted_index.verify | 59 |
| abstract_inverted_index.Bilstm. | 37 |
| abstract_inverted_index.Goshawk | 54 |
| abstract_inverted_index.Softmax | 139 |
| abstract_inverted_index.chaotic | 40 |
| abstract_inverted_index.divided | 118 |
| abstract_inverted_index.element | 31 |
| abstract_inverted_index.feature | 119 |
| abstract_inverted_index.improve | 50 |
| abstract_inverted_index.network | 6, 129 |
| abstract_inverted_index.optimal | 68 |
| abstract_inverted_index.signals | 82 |
| abstract_inverted_index.(BiLSTM) | 21 |
| abstract_inverted_index.Bayesian | 132 |
| abstract_inverted_index.Finally, | 116 |
| abstract_inverted_index.Firstly, | 38 |
| abstract_inverted_index.INGO-VMD | 74 |
| abstract_inverted_index.Northern | 53 |
| abstract_inverted_index.adaptive | 43 |
| abstract_inverted_index.classify | 144 |
| abstract_inverted_index.identify | 146 |
| abstract_inverted_index.improved | 16, 52 |
| abstract_inverted_index.Algorithm | 46, 56 |
| abstract_inverted_index.Secondly, | 73 |
| abstract_inverted_index.algorithm | 64 |
| abstract_inverted_index.calculate | 84 |
| abstract_inverted_index.category. | 150 |
| abstract_inverted_index.collected | 80 |
| abstract_inverted_index.component | 34 |
| abstract_inverted_index.decompose | 78 |
| abstract_inverted_index.determine | 66 |
| abstract_inverted_index.parameter | 9, 69 |
| abstract_inverted_index.proposed. | 23 |
| abstract_inverted_index.retaining | 109 |
| abstract_inverted_index.training, | 136 |
| abstract_inverted_index.vibration | 81 |
| abstract_inverted_index.classifier | 140 |
| abstract_inverted_index.components | 112 |
| abstract_inverted_index.short-term | 19 |
| abstract_inverted_index.Variational | 11 |
| abstract_inverted_index.combination | 70 |
| abstract_inverted_index.components, | 89 |
| abstract_inverted_index.constructed | 107 |
| abstract_inverted_index.correlation | 86 |
| abstract_inverted_index.information | 93, 120 |
| abstract_inverted_index.recognition | 26 |
| abstract_inverted_index.superiority | 61 |
| abstract_inverted_index.Optimisation | 55 |
| abstract_inverted_index.characterize | 97 |
| abstract_inverted_index.correlation. | 115 |
| abstract_inverted_index.optimization | 10, 133 |
| abstract_inverted_index.Decomposition | 13 |
| abstract_inverted_index.bidirectional | 17 |
| abstract_inverted_index.multi-feature | 92 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.55288273 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |