A multi-scale feature extraction and fusion method for diagnosing bearing faults Article Swipe
Zhixiang Chen
,
Hang Wang
,
Yuanyuan Zhou
,
Yang Yang
,
Yongbin Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.37965/jdmd.2024.560
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.37965/jdmd.2024.560
Bearing fault diagnosis is vital to safeguard the heath of rotating machinery. It can help to avoid economic losses and safe accidents in time. Effective feature extraction is the premise of diagnosing bearing faults. However, effective features characterizing the health status of bearings are difficult to extract from the raw bearing vibration signals. Furthermore, inefficient feature extraction results in substantial time wastage, making it hard to apply in real time monitoring. A novel feature extraction method for diagnosing bearing faults using multi-scale improved envelope spectrum entropy (MIESE) is proposed in this work. First, bearing vibration signals are analyzed across multiple scales, and improved envelope spectrum entropy (IESE) is extracted from these signals at each scale to form an original feature set. Subsequently, joint approximate diagonalization eigen (JADE) is applied to fuse above feature set for effectively eliminating redundancy and generated a refined feature set. Finally, the newly generated feature set is input into support vector machines (SVM) to effectively diagnose bearing health status. Two cases studies are employed to demonstrate the reliability of the proposed method. The results illustrate the proposed method can improve the stability of extracted features and increase the computational efficiency. Conflict of Interest Statement The authors declare no conflicts of interest.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.37965/jdmd.2024.560
- https://ojs.istp-press.com/dmd/article/download/560/513
- OA Status
- diamond
- Cited By
- 2
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401384119
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401384119Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.37965/jdmd.2024.560Digital Object Identifier
- Title
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A multi-scale feature extraction and fusion method for diagnosing bearing faultsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-07Full publication date if available
- Authors
-
Zhixiang Chen, Hang Wang, Yuanyuan Zhou, Yang Yang, Yongbin LiuList of authors in order
- Landing page
-
https://doi.org/10.37965/jdmd.2024.560Publisher landing page
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https://ojs.istp-press.com/dmd/article/download/560/513Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://ojs.istp-press.com/dmd/article/download/560/513Direct OA link when available
- Concepts
-
Feature extraction, Computer science, Pattern recognition (psychology), Entropy (arrow of time), Bearing (navigation), Data mining, Support vector machine, Artificial intelligence, Rolling-element bearing, Redundancy (engineering), Vibration, Operating system, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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18Number 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.real | 68 |
| abstract_inverted_index.safe | 20 |
| abstract_inverted_index.set. | 120, 143 |
| abstract_inverted_index.this | 90 |
| abstract_inverted_index.time | 60, 69 |
| abstract_inverted_index.(SVM) | 156 |
| abstract_inverted_index.above | 131 |
| abstract_inverted_index.apply | 66 |
| abstract_inverted_index.avoid | 16 |
| abstract_inverted_index.cases | 164 |
| abstract_inverted_index.eigen | 125 |
| abstract_inverted_index.fault | 1 |
| abstract_inverted_index.heath | 8 |
| abstract_inverted_index.input | 151 |
| abstract_inverted_index.joint | 122 |
| abstract_inverted_index.newly | 146 |
| abstract_inverted_index.novel | 72 |
| abstract_inverted_index.scale | 114 |
| abstract_inverted_index.these | 110 |
| abstract_inverted_index.time. | 23 |
| abstract_inverted_index.using | 80 |
| abstract_inverted_index.vital | 4 |
| abstract_inverted_index.work. | 91 |
| abstract_inverted_index.(IESE) | 106 |
| abstract_inverted_index.(JADE) | 126 |
| abstract_inverted_index.First, | 92 |
| abstract_inverted_index.across | 98 |
| abstract_inverted_index.faults | 79 |
| abstract_inverted_index.health | 39, 161 |
| abstract_inverted_index.losses | 18 |
| abstract_inverted_index.making | 62 |
| abstract_inverted_index.method | 75, 181 |
| abstract_inverted_index.status | 40 |
| abstract_inverted_index.vector | 154 |
| abstract_inverted_index.(MIESE) | 86 |
| abstract_inverted_index.Bearing | 0 |
| abstract_inverted_index.applied | 128 |
| abstract_inverted_index.authors | 199 |
| abstract_inverted_index.bearing | 32, 50, 78, 93, 160 |
| abstract_inverted_index.declare | 200 |
| abstract_inverted_index.entropy | 85, 105 |
| abstract_inverted_index.extract | 46 |
| abstract_inverted_index.faults. | 33 |
| abstract_inverted_index.feature | 25, 55, 73, 119, 132, 142, 148 |
| abstract_inverted_index.improve | 183 |
| abstract_inverted_index.method. | 175 |
| abstract_inverted_index.premise | 29 |
| abstract_inverted_index.refined | 141 |
| abstract_inverted_index.results | 57, 177 |
| abstract_inverted_index.scales, | 100 |
| abstract_inverted_index.signals | 95, 111 |
| abstract_inverted_index.status. | 162 |
| abstract_inverted_index.studies | 165 |
| abstract_inverted_index.support | 153 |
| abstract_inverted_index.Conflict | 194 |
| abstract_inverted_index.Finally, | 144 |
| abstract_inverted_index.However, | 34 |
| abstract_inverted_index.Interest | 196 |
| abstract_inverted_index.analyzed | 97 |
| abstract_inverted_index.bearings | 42 |
| abstract_inverted_index.diagnose | 159 |
| abstract_inverted_index.economic | 17 |
| abstract_inverted_index.employed | 167 |
| abstract_inverted_index.envelope | 83, 103 |
| abstract_inverted_index.features | 36, 188 |
| abstract_inverted_index.improved | 82, 102 |
| abstract_inverted_index.increase | 190 |
| abstract_inverted_index.machines | 155 |
| abstract_inverted_index.multiple | 99 |
| abstract_inverted_index.original | 118 |
| abstract_inverted_index.proposed | 88, 174, 180 |
| abstract_inverted_index.rotating | 10 |
| abstract_inverted_index.signals. | 52 |
| abstract_inverted_index.spectrum | 84, 104 |
| abstract_inverted_index.wastage, | 61 |
| abstract_inverted_index.Effective | 24 |
| abstract_inverted_index.Statement | 197 |
| abstract_inverted_index.accidents | 21 |
| abstract_inverted_index.conflicts | 202 |
| abstract_inverted_index.diagnosis | 2 |
| abstract_inverted_index.difficult | 44 |
| abstract_inverted_index.effective | 35 |
| abstract_inverted_index.extracted | 108, 187 |
| abstract_inverted_index.generated | 139, 147 |
| abstract_inverted_index.interest. | 204 |
| abstract_inverted_index.safeguard | 6 |
| abstract_inverted_index.stability | 185 |
| abstract_inverted_index.vibration | 51, 94 |
| abstract_inverted_index.diagnosing | 31, 77 |
| abstract_inverted_index.extraction | 26, 56, 74 |
| abstract_inverted_index.illustrate | 178 |
| abstract_inverted_index.machinery. | 11 |
| abstract_inverted_index.redundancy | 137 |
| abstract_inverted_index.approximate | 123 |
| abstract_inverted_index.demonstrate | 169 |
| abstract_inverted_index.effectively | 135, 158 |
| abstract_inverted_index.efficiency. | 193 |
| abstract_inverted_index.eliminating | 136 |
| abstract_inverted_index.inefficient | 54 |
| abstract_inverted_index.monitoring. | 70 |
| abstract_inverted_index.multi-scale | 81 |
| abstract_inverted_index.reliability | 171 |
| abstract_inverted_index.substantial | 59 |
| abstract_inverted_index.Furthermore, | 53 |
| abstract_inverted_index.Subsequently, | 121 |
| abstract_inverted_index.computational | 192 |
| abstract_inverted_index.characterizing | 37 |
| abstract_inverted_index.diagonalization | 124 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.75833 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |