Vibration feature extraction and fault detection method for transmission towers Article Swipe
Long Zhao
,
Zhicheng Liu
,
Peng Yuan
,
Guanru Wen
,
Xinbo Huang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1049/smt2.12179
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1049/smt2.12179
This paper presents a novel bolt looseness detection method for power transmission towers based on vibration signal analysis. The proposed method utilizes pulse excitation to extract the vibration signal of the tower, which is then adaptively decomposed using the Variational Mode Decomposition of Spider Wasp optimizer (SWVMD). This overcomes limitations of traditional Variational Mode Decomposition methods by leveraging bio‐inspired optimization to improve signal decomposition. Simulated signals processed with different optimization methods verify the superiority of the SWO approach. Field tests on a 110‐kV transmission tower further demonstrate the effectiveness of the proposed SWVMD technique for analyzing on‐site vibration data. A new improved intrinsic multiscale sample entropy feature is also introduced for bolt state characterization. A Spider Wasp Support Vector Machine classifier is developed to realize accurate bolt loosening monitoring using the extracted features. Dynamic response tests under varying bolt conditions show that the method can identify early loosening and reduce tower damage risks compared to conventional techniques. This novel vibration‐based detection framework presents an innovative application of nature‐inspired computing for power infrastructure health monitoring.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1049/smt2.12179
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/smt2.12179
- OA Status
- gold
- Cited By
- 2
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390980762
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390980762Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1049/smt2.12179Digital Object Identifier
- Title
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Vibration feature extraction and fault detection method for transmission towersWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-17Full publication date if available
- Authors
-
Long Zhao, Zhicheng Liu, Peng Yuan, Guanru Wen, Xinbo HuangList of authors in order
- Landing page
-
https://doi.org/10.1049/smt2.12179Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/smt2.12179Direct 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
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/smt2.12179Direct OA link when available
- Concepts
-
Vibration, Transmission tower, Feature extraction, Computer science, Support vector machine, Pattern recognition (psychology), Artificial intelligence, Tower, Engineering, Structural engineering, Acoustics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2989630770, https://openalex.org/W2890805926, https://openalex.org/W2982042820, https://openalex.org/W3017707779, https://openalex.org/W2792359959, https://openalex.org/W2766342013, https://openalex.org/W2807628539, https://openalex.org/W4317565261, https://openalex.org/W4386266029, https://openalex.org/W2954035106, https://openalex.org/W3017632586, https://openalex.org/W4229010399, https://openalex.org/W3027820774, https://openalex.org/W2592763418, https://openalex.org/W3172579827, https://openalex.org/W2793013236, https://openalex.org/W3000399793, https://openalex.org/W4312360230, https://openalex.org/W4367553590, https://openalex.org/W3116079431, https://openalex.org/W3163693889, https://openalex.org/W2807763442, https://openalex.org/W3159926427, https://openalex.org/W3024462273, https://openalex.org/W3194723578, https://openalex.org/W3109343305, https://openalex.org/W3157867098, https://openalex.org/W4360980642, https://openalex.org/W2053443947, https://openalex.org/W4221137473, https://openalex.org/W3007622608, https://openalex.org/W2321143615, https://openalex.org/W2067251417, https://openalex.org/W3097756851, https://openalex.org/W3035374596, https://openalex.org/W2805869613, https://openalex.org/W2904769428, https://openalex.org/W3041016892, https://openalex.org/W3199683159 |
| referenced_works_count | 39 |
| abstract_inverted_index.A | 100, 115 |
| abstract_inverted_index.a | 4, 82 |
| abstract_inverted_index.an | 164 |
| abstract_inverted_index.by | 57 |
| abstract_inverted_index.is | 34, 108, 122 |
| abstract_inverted_index.of | 30, 43, 51, 75, 90, 167 |
| abstract_inverted_index.on | 15, 81 |
| abstract_inverted_index.to | 25, 61, 124, 155 |
| abstract_inverted_index.SWO | 77 |
| abstract_inverted_index.The | 19 |
| abstract_inverted_index.and | 149 |
| abstract_inverted_index.can | 145 |
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| abstract_inverted_index.new | 101 |
| abstract_inverted_index.the | 27, 31, 39, 73, 76, 88, 91, 131, 143 |
| abstract_inverted_index.Mode | 41, 54 |
| abstract_inverted_index.This | 1, 48, 158 |
| abstract_inverted_index.Wasp | 45, 117 |
| abstract_inverted_index.also | 109 |
| abstract_inverted_index.bolt | 6, 112, 127, 139 |
| abstract_inverted_index.show | 141 |
| abstract_inverted_index.that | 142 |
| abstract_inverted_index.then | 35 |
| abstract_inverted_index.with | 68 |
| abstract_inverted_index.Field | 79 |
| abstract_inverted_index.SWVMD | 93 |
| abstract_inverted_index.based | 14 |
| abstract_inverted_index.data. | 99 |
| abstract_inverted_index.early | 147 |
| abstract_inverted_index.novel | 5, 159 |
| abstract_inverted_index.paper | 2 |
| abstract_inverted_index.power | 11, 171 |
| abstract_inverted_index.pulse | 23 |
| abstract_inverted_index.risks | 153 |
| abstract_inverted_index.state | 113 |
| abstract_inverted_index.tests | 80, 136 |
| abstract_inverted_index.tower | 85, 151 |
| abstract_inverted_index.under | 137 |
| abstract_inverted_index.using | 38, 130 |
| abstract_inverted_index.which | 33 |
| abstract_inverted_index.Spider | 44, 116 |
| abstract_inverted_index.Vector | 119 |
| abstract_inverted_index.damage | 152 |
| abstract_inverted_index.health | 173 |
| abstract_inverted_index.method | 9, 21, 144 |
| abstract_inverted_index.reduce | 150 |
| abstract_inverted_index.sample | 105 |
| abstract_inverted_index.signal | 17, 29, 63 |
| abstract_inverted_index.tower, | 32 |
| abstract_inverted_index.towers | 13 |
| abstract_inverted_index.verify | 72 |
| abstract_inverted_index.Dynamic | 134 |
| abstract_inverted_index.Machine | 120 |
| abstract_inverted_index.Support | 118 |
| abstract_inverted_index.entropy | 106 |
| abstract_inverted_index.extract | 26 |
| abstract_inverted_index.feature | 107 |
| abstract_inverted_index.further | 86 |
| abstract_inverted_index.improve | 62 |
| abstract_inverted_index.methods | 56, 71 |
| abstract_inverted_index.realize | 125 |
| abstract_inverted_index.signals | 66 |
| abstract_inverted_index.varying | 138 |
| abstract_inverted_index.(SWVMD). | 47 |
| abstract_inverted_index.110‐kV | 83 |
| abstract_inverted_index.Abstract | 0 |
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| abstract_inverted_index.compared | 154 |
| abstract_inverted_index.identify | 146 |
| abstract_inverted_index.improved | 102 |
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| abstract_inverted_index.utilizes | 22 |
| abstract_inverted_index.Simulated | 65 |
| abstract_inverted_index.analysis. | 18 |
| abstract_inverted_index.analyzing | 96 |
| abstract_inverted_index.approach. | 78 |
| abstract_inverted_index.computing | 169 |
| abstract_inverted_index.detection | 8, 161 |
| abstract_inverted_index.developed | 123 |
| abstract_inverted_index.different | 69 |
| abstract_inverted_index.extracted | 132 |
| abstract_inverted_index.features. | 133 |
| abstract_inverted_index.framework | 162 |
| abstract_inverted_index.intrinsic | 103 |
| abstract_inverted_index.looseness | 7 |
| abstract_inverted_index.loosening | 128, 148 |
| abstract_inverted_index.on‐site | 97 |
| abstract_inverted_index.optimizer | 46 |
| abstract_inverted_index.overcomes | 49 |
| abstract_inverted_index.processed | 67 |
| abstract_inverted_index.technique | 94 |
| abstract_inverted_index.vibration | 16, 28, 98 |
| abstract_inverted_index.adaptively | 36 |
| abstract_inverted_index.classifier | 121 |
| abstract_inverted_index.conditions | 140 |
| abstract_inverted_index.decomposed | 37 |
| abstract_inverted_index.excitation | 24 |
| abstract_inverted_index.innovative | 165 |
| abstract_inverted_index.introduced | 110 |
| abstract_inverted_index.leveraging | 58 |
| abstract_inverted_index.monitoring | 129 |
| abstract_inverted_index.multiscale | 104 |
| abstract_inverted_index.Variational | 40, 53 |
| abstract_inverted_index.application | 166 |
| abstract_inverted_index.demonstrate | 87 |
| abstract_inverted_index.limitations | 50 |
| abstract_inverted_index.monitoring. | 174 |
| abstract_inverted_index.superiority | 74 |
| abstract_inverted_index.techniques. | 157 |
| abstract_inverted_index.traditional | 52 |
| abstract_inverted_index.conventional | 156 |
| abstract_inverted_index.optimization | 60, 70 |
| abstract_inverted_index.transmission | 12, 84 |
| abstract_inverted_index.Decomposition | 42, 55 |
| abstract_inverted_index.effectiveness | 89 |
| abstract_inverted_index.bio‐inspired | 59 |
| abstract_inverted_index.decomposition. | 64 |
| abstract_inverted_index.infrastructure | 172 |
| abstract_inverted_index.characterization. | 114 |
| abstract_inverted_index.nature‐inspired | 168 |
| abstract_inverted_index.vibration‐based | 160 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5079591519 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I27599042 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.71273881 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |