Enhanced fault diagnosis of rolling bearings using attention-augmented separable residual networks Article Swipe
Chuang Liang
,
Xiaofeng Mu
,
Xiaoguang Zhang
,
F. X. Lu
,
Chengcheng Wang
,
Yubo Shao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jestch.2024.101930
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.jestch.2024.101930
Recently, with the quick development of industrial equipment automation, convolutional neural networks (CNN) have been broadly applied to the intelligent fault diagnosis of rolling bearings. In order to solve the problems of gradient vanishing, gradient explosion, and too many training parameters in deep convolutional networks that lead to low diagnostic accuracy and training efficiency of network models, a bearing fault diagnosis method based on an attention-augmented separable convolutional residual network (ASResnet) is proposed. First, the bearing vibration signal data is converted into a 2D grayscale map as an input to the network. Then, residual blocks with separable convolutions were constructed, allowing automatic learning of high-level representations from input images by stacking multiple separable convolutional residual blocks. Separable convolution effectively reduces the number of network parameters and improves computational speed. Finally, a feature extractor based on the Convolutional Block Attention Module (CBAM) is constructed so that the network focuses on the key feature regions to further improve the diagnostic performance. Validation was conducted using a Case Western Reserve University bearing dataset and three actual engineering datasets of production equipment in a cement plant. The experimental results show that ASResnet is able to improve the diagnostic accuracy and reduce the network training time of the CWRU dataset, and it also obtains a high accuracy rate in fault diagnosis for engineering applications in the cement production equipment industry.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jestch.2024.101930
- OA Status
- diamond
- Cited By
- 7
- References
- 43
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- OpenAlex ID
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405223936Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jestch.2024.101930Digital Object Identifier
- Title
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Enhanced fault diagnosis of rolling bearings using attention-augmented separable residual networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
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2024-12-10Full publication date if available
- Authors
-
Chuang Liang, Xiaofeng Mu, Xiaoguang Zhang, F. X. Lu, Chengcheng Wang, Yubo ShaoList of authors in order
- Landing page
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https://doi.org/10.1016/j.jestch.2024.101930Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.jestch.2024.101930Direct OA link when available
- Concepts
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Residual, Fault (geology), Separable space, Materials science, Bearing (navigation), Forensic engineering, Structural engineering, Computer science, Geology, Artificial intelligence, Engineering, Algorithm, Mathematics, Seismology, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2025: 7Per-year citation counts (last 5 years)
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43Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.into | 81 |
| abstract_inverted_index.lead | 46 |
| abstract_inverted_index.many | 38 |
| abstract_inverted_index.rate | 212 |
| abstract_inverted_index.show | 185 |
| abstract_inverted_index.that | 45, 144, 186 |
| abstract_inverted_index.time | 200 |
| abstract_inverted_index.were | 98 |
| abstract_inverted_index.with | 1, 95 |
| abstract_inverted_index.(CNN) | 12 |
| abstract_inverted_index.Block | 137 |
| abstract_inverted_index.Then, | 92 |
| abstract_inverted_index.based | 62, 133 |
| abstract_inverted_index.fault | 20, 59, 214 |
| abstract_inverted_index.input | 88, 107 |
| abstract_inverted_index.order | 26 |
| abstract_inverted_index.quick | 3 |
| abstract_inverted_index.solve | 28 |
| abstract_inverted_index.three | 171 |
| abstract_inverted_index.using | 162 |
| abstract_inverted_index.(CBAM) | 140 |
| abstract_inverted_index.First, | 73 |
| abstract_inverted_index.Module | 139 |
| abstract_inverted_index.actual | 172 |
| abstract_inverted_index.blocks | 94 |
| abstract_inverted_index.cement | 180, 221 |
| abstract_inverted_index.images | 108 |
| abstract_inverted_index.method | 61 |
| abstract_inverted_index.neural | 10 |
| abstract_inverted_index.number | 121 |
| abstract_inverted_index.plant. | 181 |
| abstract_inverted_index.reduce | 196 |
| abstract_inverted_index.signal | 77 |
| abstract_inverted_index.speed. | 128 |
| abstract_inverted_index.Reserve | 166 |
| abstract_inverted_index.Western | 165 |
| abstract_inverted_index.applied | 16 |
| abstract_inverted_index.bearing | 58, 75, 168 |
| abstract_inverted_index.blocks. | 115 |
| abstract_inverted_index.broadly | 15 |
| abstract_inverted_index.dataset | 169 |
| abstract_inverted_index.feature | 131, 151 |
| abstract_inverted_index.focuses | 147 |
| abstract_inverted_index.further | 154 |
| abstract_inverted_index.improve | 155, 191 |
| abstract_inverted_index.models, | 56 |
| abstract_inverted_index.network | 55, 69, 123, 146, 198 |
| abstract_inverted_index.obtains | 208 |
| abstract_inverted_index.reduces | 119 |
| abstract_inverted_index.regions | 152 |
| abstract_inverted_index.results | 184 |
| abstract_inverted_index.rolling | 23 |
| abstract_inverted_index.ASResnet | 187 |
| abstract_inverted_index.Finally, | 129 |
| abstract_inverted_index.accuracy | 50, 194, 211 |
| abstract_inverted_index.allowing | 100 |
| abstract_inverted_index.dataset, | 204 |
| abstract_inverted_index.datasets | 174 |
| abstract_inverted_index.gradient | 32, 34 |
| abstract_inverted_index.improves | 126 |
| abstract_inverted_index.learning | 102 |
| abstract_inverted_index.multiple | 111 |
| abstract_inverted_index.network. | 91 |
| abstract_inverted_index.networks | 11, 44 |
| abstract_inverted_index.problems | 30 |
| abstract_inverted_index.residual | 68, 93, 114 |
| abstract_inverted_index.stacking | 110 |
| abstract_inverted_index.training | 39, 52, 199 |
| abstract_inverted_index.Attention | 138 |
| abstract_inverted_index.Recently, | 0 |
| abstract_inverted_index.Separable | 116 |
| abstract_inverted_index.automatic | 101 |
| abstract_inverted_index.bearings. | 24 |
| abstract_inverted_index.conducted | 161 |
| abstract_inverted_index.converted | 80 |
| abstract_inverted_index.diagnosis | 21, 60, 215 |
| abstract_inverted_index.equipment | 7, 177, 223 |
| abstract_inverted_index.extractor | 132 |
| abstract_inverted_index.grayscale | 84 |
| abstract_inverted_index.industry. | 224 |
| abstract_inverted_index.proposed. | 72 |
| abstract_inverted_index.separable | 66, 96, 112 |
| abstract_inverted_index.vibration | 76 |
| abstract_inverted_index.(ASResnet) | 70 |
| abstract_inverted_index.University | 167 |
| abstract_inverted_index.Validation | 159 |
| abstract_inverted_index.diagnostic | 49, 157, 193 |
| abstract_inverted_index.efficiency | 53 |
| abstract_inverted_index.explosion, | 35 |
| abstract_inverted_index.high-level | 104 |
| abstract_inverted_index.industrial | 6 |
| abstract_inverted_index.parameters | 40, 124 |
| abstract_inverted_index.production | 176, 222 |
| abstract_inverted_index.vanishing, | 33 |
| abstract_inverted_index.automation, | 8 |
| abstract_inverted_index.constructed | 142 |
| abstract_inverted_index.convolution | 117 |
| abstract_inverted_index.development | 4 |
| abstract_inverted_index.effectively | 118 |
| abstract_inverted_index.engineering | 173, 217 |
| abstract_inverted_index.intelligent | 19 |
| abstract_inverted_index.applications | 218 |
| abstract_inverted_index.constructed, | 99 |
| abstract_inverted_index.convolutions | 97 |
| abstract_inverted_index.experimental | 183 |
| abstract_inverted_index.performance. | 158 |
| abstract_inverted_index.Convolutional | 136 |
| abstract_inverted_index.computational | 127 |
| abstract_inverted_index.convolutional | 9, 43, 67, 113 |
| abstract_inverted_index.representations | 105 |
| abstract_inverted_index.attention-augmented | 65 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.6299999952316284 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.92583765 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |