A new multiple mixed augmentation-based transfer learning method for machinery fault diagnosis Article Swipe
Hangqi Ge
,
Changqing Shen
,
Xinhai Lin
,
Dong Wang
,
Juanjuan Shi
,
Weiguo Huang
,
Zhongkui Zhu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad4d15
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad4d15
With the continuous development of various industries, the diagnosis of industrial equipment faults has been receiving increasing attention in recent years. Considering the complex and variable working conditions, and the limited amount of fault data, transfer learning has become an effective solution for fault diagnosis. Data augmentation techniques, particularly generative adversarial networks, have achieved tremendous development within the field of transfer learning fault diagnosis. However, traditional data augmentation methods experience difficulty in extracting features conducive to fault diagnosis from fault data under complex operating conditions, particularly in the case of raw vibration data from bearings. Therefore, this study proposes a new multiple mixed augmentation-based transfer learning (MMATL) method for machinery fault diagnosis. First, an augmentation chain that dynamically adjusts data augmentation strategies in accordance with the model’s performance is constructed based on AutoAugment. Then, a multiple mixed augmentation strategy that integrates fault data into the augmented data from the augmentation chain to obtain enhanced data suitable for training is proposed. This strategy consists of multiple augmentations, augmentation mixing, and data mixing. Finally, experiments confirm the effectiveness of MMATL on the bearing datasets from the gearbox of the Chinese CRH380A high-speed train, the test rig at the University of Paderborn in Germany and the self-made bearing failure test platform. Results indicate that the method can adaptively extract features from fault data that are conducive to fault diagnosis under complex operating conditions.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6501/ad4d15
- https://iopscience.iop.org/article/10.1088/1361-6501/ad4d15/pdf
- OA Status
- hybrid
- Cited By
- 7
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396991208
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4396991208Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1361-6501/ad4d15Digital Object Identifier
- Title
-
A new multiple mixed augmentation-based transfer learning method for machinery fault diagnosisWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-17Full publication date if available
- Authors
-
Hangqi Ge, Changqing Shen, Xinhai Lin, Dong Wang, Juanjuan Shi, Weiguo Huang, Zhongkui ZhuList of authors in order
- Landing page
-
https://doi.org/10.1088/1361-6501/ad4d15Publisher landing page
- PDF URL
-
https://iopscience.iop.org/article/10.1088/1361-6501/ad4d15/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://iopscience.iop.org/article/10.1088/1361-6501/ad4d15/pdfDirect OA link when available
- Concepts
-
Transfer (computing), Computer science, Fault (geology), Transfer of learning, Artificial intelligence, Geology, Parallel computing, SeismologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 3Per-year citation counts (last 5 years)
- References (count)
-
29Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4396991208 |
|---|---|
| doi | https://doi.org/10.1088/1361-6501/ad4d15 |
| ids.doi | https://doi.org/10.1088/1361-6501/ad4d15 |
| ids.openalex | https://openalex.org/W4396991208 |
| fwci | 4.45346079 |
| type | article |
| title | A new multiple mixed augmentation-based transfer learning method for machinery fault diagnosis |
| awards[0].id | https://openalex.org/G5743719762 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 52175056 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 8 |
| biblio.volume | 35 |
| biblio.last_page | 086141 |
| biblio.first_page | 086141 |
| topics[0].id | https://openalex.org/T13717 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9758999943733215 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2207 |
| topics[0].subfield.display_name | Control and Systems Engineering |
| topics[0].display_name | Advanced Algorithms and Applications |
| topics[1].id | https://openalex.org/T14225 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9660000205039978 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2207 |
| topics[1].subfield.display_name | Control and Systems Engineering |
| topics[1].display_name | Advanced Sensor and Control Systems |
| topics[2].id | https://openalex.org/T10876 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9574999809265137 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2207 |
| topics[2].subfield.display_name | Control and Systems Engineering |
| topics[2].display_name | Fault Detection and Control Systems |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2776175482 |
| concepts[0].level | 2 |
| concepts[0].score | 0.5955718159675598 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1195816 |
| concepts[0].display_name | Transfer (computing) |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5906214714050293 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C175551986 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5562978982925415 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q47089 |
| concepts[2].display_name | Fault (geology) |
| concepts[3].id | https://openalex.org/C150899416 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5142599940299988 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q1820378 |
| concepts[3].display_name | Transfer of learning |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.29943200945854187 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C127313418 |
| concepts[5].level | 0 |
| concepts[5].score | 0.14077043533325195 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[5].display_name | Geology |
| concepts[6].id | https://openalex.org/C173608175 |
| concepts[6].level | 1 |
| concepts[6].score | 0.1048855185508728 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q232661 |
| concepts[6].display_name | Parallel computing |
| concepts[7].id | https://openalex.org/C165205528 |
| concepts[7].level | 1 |
| concepts[7].score | 0.06806501746177673 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q83371 |
| concepts[7].display_name | Seismology |
| keywords[0].id | https://openalex.org/keywords/transfer |
| keywords[0].score | 0.5955718159675598 |
| keywords[0].display_name | Transfer (computing) |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5906214714050293 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/fault |
| keywords[2].score | 0.5562978982925415 |
| keywords[2].display_name | Fault (geology) |
| keywords[3].id | https://openalex.org/keywords/transfer-of-learning |
| keywords[3].score | 0.5142599940299988 |
| keywords[3].display_name | Transfer of learning |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.29943200945854187 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/geology |
| keywords[5].score | 0.14077043533325195 |
| keywords[5].display_name | Geology |
| keywords[6].id | https://openalex.org/keywords/parallel-computing |
| keywords[6].score | 0.1048855185508728 |
| keywords[6].display_name | Parallel computing |
| keywords[7].id | https://openalex.org/keywords/seismology |
| keywords[7].score | 0.06806501746177673 |
| keywords[7].display_name | Seismology |
| language | en |
| locations[0].id | doi:10.1088/1361-6501/ad4d15 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S109302362 |
| locations[0].source.issn | 0957-0233, 1361-6501 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0957-0233 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Measurement Science and Technology |
| locations[0].source.host_organization | https://openalex.org/P4310320083 |
| locations[0].source.host_organization_name | IOP Publishing |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| locations[0].source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://iopscience.iop.org/article/10.1088/1361-6501/ad4d15/pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Measurement Science and Technology |
| locations[0].landing_page_url | https://doi.org/10.1088/1361-6501/ad4d15 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5113210597 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Hangqi Ge |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I3923682 |
| authorships[0].affiliations[0].raw_affiliation_string | Soochow University School of Rail Transportation, Soochow University Suzhou, Suzhou, Jiangsu, 215131, CHINA |
| authorships[0].institutions[0].id | https://openalex.org/I3923682 |
| authorships[0].institutions[0].ror | https://ror.org/05t8y2r12 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I3923682 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Soochow University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hangqi Ge |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Soochow University School of Rail Transportation, Soochow University Suzhou, Suzhou, Jiangsu, 215131, CHINA |
| authorships[1].author.id | https://openalex.org/A5091072755 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5143-8366 |
| authorships[1].author.display_name | Changqing Shen |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I3923682 |
| authorships[1].affiliations[0].raw_affiliation_string | Soochow University School of Rail Transportation, Soochow University Suzhou, Suzhou, Jiangsu, 215131, CHINA |
| authorships[1].institutions[0].id | https://openalex.org/I3923682 |
| authorships[1].institutions[0].ror | https://ror.org/05t8y2r12 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I3923682 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Soochow University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Changqing Shen |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Soochow University School of Rail Transportation, Soochow University Suzhou, Suzhou, Jiangsu, 215131, CHINA |
| authorships[2].author.id | https://openalex.org/A5114161807 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Xinhai Lin |
| authorships[2].affiliations[0].raw_affiliation_string | CRRC Qingdao Sifang Rolling Stock Research Institute Co Ltd, Changzhou, Qingdao, Shandong, 213011, CHINA |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Xinhai Lin |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | CRRC Qingdao Sifang Rolling Stock Research Institute Co Ltd, Changzhou, Qingdao, Shandong, 213011, CHINA |
| authorships[3].author.id | https://openalex.org/A5100391562 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4872-4860 |
| authorships[3].author.display_name | Dong Wang |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I183067930, https://openalex.org/I4391768146 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Industrial Engineering and Management, Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration, 800 Dongchuan Road, Shanghai, Shanghai, 200240, CHINA |
| authorships[3].institutions[0].id | https://openalex.org/I4391768146 |
| authorships[3].institutions[0].ror | https://ror.org/02hphne60 |
| authorships[3].institutions[0].type | facility |
| authorships[3].institutions[0].lineage | https://openalex.org/I183067930, https://openalex.org/I4391768146 |
| authorships[3].institutions[0].country_code | |
| authorships[3].institutions[0].display_name | State Key Laboratory of Mechanical System and Vibration |
| authorships[3].institutions[1].id | https://openalex.org/I183067930 |
| authorships[3].institutions[1].ror | https://ror.org/0220qvk04 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I183067930 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | Shanghai Jiao Tong University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Dong Wang |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Industrial Engineering and Management, Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration, 800 Dongchuan Road, Shanghai, Shanghai, 200240, CHINA |
| authorships[4].author.id | https://openalex.org/A5043608454 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-8634-9083 |
| authorships[4].author.display_name | Juanjuan Shi |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I3923682 |
| authorships[4].affiliations[0].raw_affiliation_string | Faculty of Engineering, Soochow University School of Rail Transportation, 161 Louis Pasteur, Suzhou, Jiangsu, 215131, CHINA |
| authorships[4].institutions[0].id | https://openalex.org/I3923682 |
| authorships[4].institutions[0].ror | https://ror.org/05t8y2r12 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I3923682 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Soochow University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Juanjuan Shi |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Faculty of Engineering, Soochow University School of Rail Transportation, 161 Louis Pasteur, Suzhou, Jiangsu, 215131, CHINA |
| authorships[5].author.id | https://openalex.org/A5072145928 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-6734-2019 |
| authorships[5].author.display_name | Weiguo Huang |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I3923682 |
| authorships[5].affiliations[0].raw_affiliation_string | Soochow University School of Rail Transportation, Soochow University, Suzhou, Jiangsu, 215131, CHINA |
| authorships[5].institutions[0].id | https://openalex.org/I3923682 |
| authorships[5].institutions[0].ror | https://ror.org/05t8y2r12 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I3923682 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Soochow University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Weiguo Huang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Soochow University School of Rail Transportation, Soochow University, Suzhou, Jiangsu, 215131, CHINA |
| authorships[6].author.id | https://openalex.org/A5100758788 |
| authorships[6].author.orcid | https://orcid.org/0000-0001-9827-4154 |
| authorships[6].author.display_name | Zhongkui Zhu |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I3923682 |
| authorships[6].affiliations[0].raw_affiliation_string | Soochow University School of Rail Transportation, Soochow University, Suzhou, Jiangsu, 215131, CHINA |
| authorships[6].institutions[0].id | https://openalex.org/I3923682 |
| authorships[6].institutions[0].ror | https://ror.org/05t8y2r12 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I3923682 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Soochow University |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Zhongkui Zhu |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Soochow University School of Rail Transportation, Soochow University, Suzhou, Jiangsu, 215131, CHINA |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://iopscience.iop.org/article/10.1088/1361-6501/ad4d15/pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A new multiple mixed augmentation-based transfer learning method for machinery fault diagnosis |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T13717 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9758999943733215 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2207 |
| primary_topic.subfield.display_name | Control and Systems Engineering |
| primary_topic.display_name | Advanced Algorithms and Applications |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W2358668433, https://openalex.org/W4396701345, https://openalex.org/W2376932109, https://openalex.org/W2001405890, https://openalex.org/W4396696052, https://openalex.org/W2382290278, https://openalex.org/W4395014643 |
| cited_by_count | 7 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 4 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 3 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1088/1361-6501/ad4d15 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S109302362 |
| best_oa_location.source.issn | 0957-0233, 1361-6501 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0957-0233 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Measurement Science and Technology |
| best_oa_location.source.host_organization | https://openalex.org/P4310320083 |
| best_oa_location.source.host_organization_name | IOP Publishing |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| best_oa_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://iopscience.iop.org/article/10.1088/1361-6501/ad4d15/pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Measurement Science and Technology |
| best_oa_location.landing_page_url | https://doi.org/10.1088/1361-6501/ad4d15 |
| primary_location.id | doi:10.1088/1361-6501/ad4d15 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S109302362 |
| primary_location.source.issn | 0957-0233, 1361-6501 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0957-0233 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Measurement Science and Technology |
| primary_location.source.host_organization | https://openalex.org/P4310320083 |
| primary_location.source.host_organization_name | IOP Publishing |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| primary_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://iopscience.iop.org/article/10.1088/1361-6501/ad4d15/pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Measurement Science and Technology |
| primary_location.landing_page_url | https://doi.org/10.1088/1361-6501/ad4d15 |
| publication_date | 2024-05-17 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4387899032, https://openalex.org/W4323767075, https://openalex.org/W4389284049, https://openalex.org/W2998506103, https://openalex.org/W4225842653, https://openalex.org/W3112955596, https://openalex.org/W3110068079, https://openalex.org/W4303710087, https://openalex.org/W4385392099, https://openalex.org/W3090682168, https://openalex.org/W3008309516, https://openalex.org/W3083531094, https://openalex.org/W3083358932, https://openalex.org/W3095042947, https://openalex.org/W4304689967, https://openalex.org/W4285121936, https://openalex.org/W4388623097, https://openalex.org/W2961333734, https://openalex.org/W4285678828, https://openalex.org/W3025888249, https://openalex.org/W6763882748, https://openalex.org/W2964137095, https://openalex.org/W6729342207, https://openalex.org/W6725739302, https://openalex.org/W6677995690, https://openalex.org/W2123045220, https://openalex.org/W2949736877, https://openalex.org/W2963446712, https://openalex.org/W2549139847 |
| referenced_works_count | 29 |
| abstract_inverted_index.a | 100, 135 |
| abstract_inverted_index.an | 40, 114 |
| abstract_inverted_index.at | 195 |
| abstract_inverted_index.in | 19, 72, 87, 123, 200 |
| abstract_inverted_index.is | 129, 159 |
| abstract_inverted_index.of | 5, 10, 33, 60, 90, 164, 177, 186, 198 |
| abstract_inverted_index.on | 132, 179 |
| abstract_inverted_index.to | 76, 152, 224 |
| abstract_inverted_index.and | 25, 29, 169, 202 |
| abstract_inverted_index.are | 222 |
| abstract_inverted_index.can | 214 |
| abstract_inverted_index.for | 43, 109, 157 |
| abstract_inverted_index.has | 14, 38 |
| abstract_inverted_index.new | 101 |
| abstract_inverted_index.raw | 91 |
| abstract_inverted_index.rig | 194 |
| abstract_inverted_index.the | 2, 8, 23, 30, 58, 88, 126, 145, 149, 175, 180, 184, 187, 192, 196, 203, 212 |
| abstract_inverted_index.Data | 46 |
| abstract_inverted_index.This | 161 |
| abstract_inverted_index.With | 1 |
| abstract_inverted_index.been | 15 |
| abstract_inverted_index.case | 89 |
| abstract_inverted_index.data | 67, 81, 93, 120, 143, 147, 155, 170, 220 |
| abstract_inverted_index.from | 79, 94, 148, 183, 218 |
| abstract_inverted_index.have | 53 |
| abstract_inverted_index.into | 144 |
| abstract_inverted_index.test | 193, 207 |
| abstract_inverted_index.that | 117, 140, 211, 221 |
| abstract_inverted_index.this | 97 |
| abstract_inverted_index.with | 125 |
| abstract_inverted_index.MMATL | 178 |
| abstract_inverted_index.Then, | 134 |
| abstract_inverted_index.based | 131 |
| abstract_inverted_index.chain | 116, 151 |
| abstract_inverted_index.data, | 35 |
| abstract_inverted_index.fault | 34, 44, 63, 77, 80, 111, 142, 219, 225 |
| abstract_inverted_index.field | 59 |
| abstract_inverted_index.mixed | 103, 137 |
| abstract_inverted_index.study | 98 |
| abstract_inverted_index.under | 82, 227 |
| abstract_inverted_index.First, | 113 |
| abstract_inverted_index.amount | 32 |
| abstract_inverted_index.become | 39 |
| abstract_inverted_index.faults | 13 |
| abstract_inverted_index.method | 108, 213 |
| abstract_inverted_index.obtain | 153 |
| abstract_inverted_index.recent | 20 |
| abstract_inverted_index.train, | 191 |
| abstract_inverted_index.within | 57 |
| abstract_inverted_index.years. | 21 |
| abstract_inverted_index.(MMATL) | 107 |
| abstract_inverted_index.CRH380A | 189 |
| abstract_inverted_index.Chinese | 188 |
| abstract_inverted_index.Germany | 201 |
| abstract_inverted_index.Results | 209 |
| abstract_inverted_index.adjusts | 119 |
| abstract_inverted_index.bearing | 181, 205 |
| abstract_inverted_index.complex | 24, 83, 228 |
| abstract_inverted_index.confirm | 174 |
| abstract_inverted_index.extract | 216 |
| abstract_inverted_index.failure | 206 |
| abstract_inverted_index.gearbox | 185 |
| abstract_inverted_index.limited | 31 |
| abstract_inverted_index.methods | 69 |
| abstract_inverted_index.mixing, | 168 |
| abstract_inverted_index.mixing. | 171 |
| abstract_inverted_index.various | 6 |
| abstract_inverted_index.working | 27 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Finally, | 172 |
| abstract_inverted_index.However, | 65 |
| abstract_inverted_index.achieved | 54 |
| abstract_inverted_index.consists | 163 |
| abstract_inverted_index.datasets | 182 |
| abstract_inverted_index.enhanced | 154 |
| abstract_inverted_index.features | 74, 217 |
| abstract_inverted_index.indicate | 210 |
| abstract_inverted_index.learning | 37, 62, 106 |
| abstract_inverted_index.multiple | 102, 136, 165 |
| abstract_inverted_index.proposes | 99 |
| abstract_inverted_index.solution | 42 |
| abstract_inverted_index.strategy | 139, 162 |
| abstract_inverted_index.suitable | 156 |
| abstract_inverted_index.training | 158 |
| abstract_inverted_index.transfer | 36, 61, 105 |
| abstract_inverted_index.variable | 26 |
| abstract_inverted_index.Paderborn | 199 |
| abstract_inverted_index.attention | 18 |
| abstract_inverted_index.augmented | 146 |
| abstract_inverted_index.bearings. | 95 |
| abstract_inverted_index.conducive | 75, 223 |
| abstract_inverted_index.diagnosis | 9, 78, 226 |
| abstract_inverted_index.effective | 41 |
| abstract_inverted_index.equipment | 12 |
| abstract_inverted_index.machinery | 110 |
| abstract_inverted_index.model’s | 127 |
| abstract_inverted_index.networks, | 52 |
| abstract_inverted_index.operating | 84, 229 |
| abstract_inverted_index.platform. | 208 |
| abstract_inverted_index.proposed. | 160 |
| abstract_inverted_index.receiving | 16 |
| abstract_inverted_index.self-made | 204 |
| abstract_inverted_index.vibration | 92 |
| abstract_inverted_index.Therefore, | 96 |
| abstract_inverted_index.University | 197 |
| abstract_inverted_index.accordance | 124 |
| abstract_inverted_index.adaptively | 215 |
| abstract_inverted_index.continuous | 3 |
| abstract_inverted_index.diagnosis. | 45, 64, 112 |
| abstract_inverted_index.difficulty | 71 |
| abstract_inverted_index.experience | 70 |
| abstract_inverted_index.extracting | 73 |
| abstract_inverted_index.generative | 50 |
| abstract_inverted_index.high-speed | 190 |
| abstract_inverted_index.increasing | 17 |
| abstract_inverted_index.industrial | 11 |
| abstract_inverted_index.integrates | 141 |
| abstract_inverted_index.strategies | 122 |
| abstract_inverted_index.tremendous | 55 |
| abstract_inverted_index.Considering | 22 |
| abstract_inverted_index.adversarial | 51 |
| abstract_inverted_index.conditions, | 28, 85 |
| abstract_inverted_index.conditions. | 230 |
| abstract_inverted_index.constructed | 130 |
| abstract_inverted_index.development | 4, 56 |
| abstract_inverted_index.dynamically | 118 |
| abstract_inverted_index.experiments | 173 |
| abstract_inverted_index.industries, | 7 |
| abstract_inverted_index.performance | 128 |
| abstract_inverted_index.techniques, | 48 |
| abstract_inverted_index.traditional | 66 |
| abstract_inverted_index.AutoAugment. | 133 |
| abstract_inverted_index.augmentation | 47, 68, 115, 121, 138, 150, 167 |
| abstract_inverted_index.particularly | 49, 86 |
| abstract_inverted_index.effectiveness | 176 |
| abstract_inverted_index.augmentations, | 166 |
| abstract_inverted_index.augmentation-based | 104 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5091072755 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I3923682 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.91691652 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |