Fault Diagnosis Method Based on CND-SMOTE and BA-SVM Algorithm Article Swipe
Sheng Wang
,
Liling Ma
,
Junzheng Wang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2493/1/012008
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2493/1/012008
The problem of unbalanced data classification has gotten extensive attention in the past few years. Unbalanced sample data makes the fault diagnosis and classification accuracy rate low, and the capability to classify minority-class fault samples is restricted. To address the problem that the classification algorithm in machine learning has the insufficient capability to identify minority class samples for unbalanced sample data classification problems. Therefore, this paper proposes an improved support vector machine (SVM) classification method based on the synthetic minority over-sampling technique (SMOTE). For the sampler, an improved synthetic minority over-sampling technique based on the characteristics of neighborhood distribution (CND-SMOTE) algorithm is used to equilibrate the minority class samples and the majority class samples. For the classifier, the parameter optimization method of support vector machines based on the bat algorithm (BA-SVM) is used to solve the multi-classification problem of faulty samples. Finally, experimental results prove that the CND-SMOTE+BA-SVM algorithm can synthesize high-quality minority fault samples, increase the classification accuracy rate of fault samples, and decrease the time spent on the classification.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2493/1/012008
- https://iopscience.iop.org/article/10.1088/1742-6596/2493/1/012008/pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 10
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376608833
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4376608833Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/2493/1/012008Digital Object Identifier
- Title
-
Fault Diagnosis Method Based on CND-SMOTE and BA-SVM AlgorithmWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-01Full publication date if available
- Authors
-
Sheng Wang, Liling Ma, Junzheng WangList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2493/1/012008Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1742-6596/2493/1/012008/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://iopscience.iop.org/article/10.1088/1742-6596/2493/1/012008/pdfDirect OA link when available
- Concepts
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Support vector machine, Classifier (UML), Artificial intelligence, Computer science, Algorithm, Fault (geology), Machine learning, Sample (material), Statistical classification, Data mining, Pattern recognition (psychology), Geology, Seismology, Chromatography, ChemistryTop 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)
-
10Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.has | 7, 49 |
| abstract_inverted_index.the | 12, 20, 29, 40, 43, 50, 78, 85, 95, 106, 111, 116, 118, 128, 136, 147, 157, 166, 170 |
| abstract_inverted_index.data | 5, 18, 61 |
| abstract_inverted_index.low, | 27 |
| abstract_inverted_index.past | 13 |
| abstract_inverted_index.rate | 26, 160 |
| abstract_inverted_index.that | 42, 146 |
| abstract_inverted_index.this | 65 |
| abstract_inverted_index.time | 167 |
| abstract_inverted_index.used | 103, 133 |
| abstract_inverted_index.(SVM) | 73 |
| abstract_inverted_index.based | 76, 93, 126 |
| abstract_inverted_index.class | 56, 108, 113 |
| abstract_inverted_index.fault | 21, 34, 154, 162 |
| abstract_inverted_index.makes | 19 |
| abstract_inverted_index.paper | 66 |
| abstract_inverted_index.prove | 145 |
| abstract_inverted_index.solve | 135 |
| abstract_inverted_index.spent | 168 |
| abstract_inverted_index.faulty | 140 |
| abstract_inverted_index.gotten | 8 |
| abstract_inverted_index.method | 75, 121 |
| abstract_inverted_index.sample | 17, 60 |
| abstract_inverted_index.vector | 71, 124 |
| abstract_inverted_index.years. | 15 |
| abstract_inverted_index.address | 39 |
| abstract_inverted_index.machine | 47, 72 |
| abstract_inverted_index.problem | 2, 41, 138 |
| abstract_inverted_index.results | 144 |
| abstract_inverted_index.samples | 35, 57, 109 |
| abstract_inverted_index.support | 70, 123 |
| abstract_inverted_index.(BA-SVM) | 131 |
| abstract_inverted_index.(SMOTE). | 83 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Finally, | 142 |
| abstract_inverted_index.accuracy | 25, 159 |
| abstract_inverted_index.classify | 32 |
| abstract_inverted_index.decrease | 165 |
| abstract_inverted_index.identify | 54 |
| abstract_inverted_index.improved | 69, 88 |
| abstract_inverted_index.increase | 156 |
| abstract_inverted_index.learning | 48 |
| abstract_inverted_index.machines | 125 |
| abstract_inverted_index.majority | 112 |
| abstract_inverted_index.minority | 55, 80, 90, 107, 153 |
| abstract_inverted_index.proposes | 67 |
| abstract_inverted_index.sampler, | 86 |
| abstract_inverted_index.samples, | 155, 163 |
| abstract_inverted_index.samples. | 114, 141 |
| abstract_inverted_index.algorithm | 45, 101, 130, 149 |
| abstract_inverted_index.attention | 10 |
| abstract_inverted_index.diagnosis | 22 |
| abstract_inverted_index.extensive | 9 |
| abstract_inverted_index.parameter | 119 |
| abstract_inverted_index.problems. | 63 |
| abstract_inverted_index.synthetic | 79, 89 |
| abstract_inverted_index.technique | 82, 92 |
| abstract_inverted_index.Therefore, | 64 |
| abstract_inverted_index.Unbalanced | 16 |
| abstract_inverted_index.capability | 30, 52 |
| abstract_inverted_index.synthesize | 151 |
| abstract_inverted_index.unbalanced | 4, 59 |
| abstract_inverted_index.(CND-SMOTE) | 100 |
| abstract_inverted_index.classifier, | 117 |
| abstract_inverted_index.equilibrate | 105 |
| abstract_inverted_index.restricted. | 37 |
| abstract_inverted_index.distribution | 99 |
| abstract_inverted_index.experimental | 143 |
| abstract_inverted_index.high-quality | 152 |
| abstract_inverted_index.insufficient | 51 |
| abstract_inverted_index.neighborhood | 98 |
| abstract_inverted_index.optimization | 120 |
| abstract_inverted_index.over-sampling | 81, 91 |
| abstract_inverted_index.classification | 6, 24, 44, 62, 74, 158 |
| abstract_inverted_index.minority-class | 33 |
| abstract_inverted_index.characteristics | 96 |
| abstract_inverted_index.classification. | 171 |
| abstract_inverted_index.CND-SMOTE+BA-SVM | 148 |
| abstract_inverted_index.multi-classification | 137 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5100371313 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I125839683, https://openalex.org/I890469752 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| sustainable_development_goals[1].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[1].score | 0.4000000059604645 |
| sustainable_development_goals[1].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.65701415 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |