Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring Article Swipe
Bingyan Chen
,
Fengshou Gu
,
Weihua Zhang
,
Dongli Song
,
Yao Cheng
,
Zewen Zhou
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1177/14759217221149745
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1177/14759217221149745
The Gini index (GI), GI II, and GI III are proven to be effective sparsity measures in the fields of machine condition monitoring and fault diagnosis, and they can be reformulated as the ratio of different quasi-arithmetic means (RQAM). Under this framework, generalized Gini indices (GGIs) have been developed for sparse quantification by applying nonlinear weights to GI, and another generalized form of GI, referred to here as power function-based Gini indices I (PFGI1s), has been introduced by using power function as the generator of quasi-arithmetic means. The GGIs with different weight parameters exhibit reliable sparse quantization capability for repetitive transient features, while their repetitive transient discriminability is lower than kurtosis and negentropy under noise contamination. PFGI1 achieves enhanced repetitive transient discriminability with increasing power exponent, showing the advantage of the generalization approach. In this paper, based on RQAM, a single-parameter generalization method for generating PFGI1s is introduced into GI II and GI III from the perspective of the quasi-arithmetic mean generator, which leads to the power function-based Gini indices II and III (PFGI2s and PFGI3s) constructed from GI II and GI III, respectively. Mathematical derivation proves that PFGI2s and PFGI3s satisfy at least five of six typical attributes of sparsity measures and are two new families of sparsity measures. Simulation analysis shows that, similar to PFGI1s, PFGI2s and PFGI3s can monotonically estimate the sparsity of the data sequence and can simultaneously achieve strong random transient resistibility and high repetitive transient discriminability compared with traditional sparsity measures. The experimental results of bearing run-to-failure demonstrate that PFGI1s, PFGI2s, and PFGI3s with appropriate power exponents can effectively quantify the repetitive transient features caused by bearing faults and can accurately characterize the bearing degradation status compared with the state-of-the-art sparsity measures.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/14759217221149745
- OA Status
- green
- Cited By
- 13
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4323050284
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4323050284Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1177/14759217221149745Digital Object Identifier
- Title
-
Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoringWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-04Full publication date if available
- Authors
-
Bingyan Chen, Fengshou Gu, Weihua Zhang, Dongli Song, Yao Cheng, Zewen ZhouList of authors in order
- Landing page
-
https://doi.org/10.1177/14759217221149745Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://pure.hud.ac.uk/ws/files/54564448/Manuscript_SHM_22_0384_Final_Version.pdfDirect OA link when available
- Concepts
-
Mathematics, Kurtosis, Generalization, Quantile, Algorithm, Transient (computer programming), Exponent, Statistics, Computer science, Philosophy, Linguistics, Operating system, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 9, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4323050284 |
|---|---|
| doi | https://doi.org/10.1177/14759217221149745 |
| ids.doi | https://doi.org/10.1177/14759217221149745 |
| ids.openalex | https://openalex.org/W4323050284 |
| fwci | 3.23509031 |
| type | article |
| title | Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring |
| awards[0].id | https://openalex.org/G2789385588 |
| awards[0].funder_id | https://openalex.org/F4320327029 |
| awards[0].display_name | |
| awards[0].funder_award_id | TPL2210 |
| awards[0].funder_display_name | State Key Laboratory of Traction Power |
| awards[1].id | https://openalex.org/G62964174 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | 52275133 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| awards[2].id | https://openalex.org/G1772788129 |
| awards[2].funder_id | https://openalex.org/F4320322725 |
| awards[2].display_name | |
| awards[2].funder_award_id | 202107000033 |
| awards[2].funder_display_name | China Scholarship Council |
| awards[3].id | https://openalex.org/G3851963265 |
| awards[3].funder_id | https://openalex.org/F4320322720 |
| awards[3].display_name | |
| awards[3].funder_award_id | 2682021CX090 |
| awards[3].funder_display_name | Southwest Jiaotong University |
| awards[4].id | https://openalex.org/G4199430986 |
| awards[4].funder_id | https://openalex.org/F4320321540 |
| awards[4].display_name | |
| awards[4].funder_award_id | 2021YFB3400704-02 |
| awards[4].funder_display_name | Ministry of Science and Technology of the People's Republic of China |
| biblio.issue | 6 |
| biblio.volume | 22 |
| biblio.last_page | 3706 |
| biblio.first_page | 3677 |
| topics[0].id | https://openalex.org/T10220 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9995999932289124 |
| 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 | Machine Fault Diagnosis Techniques |
| topics[1].id | https://openalex.org/T12169 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9970999956130981 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2210 |
| topics[1].subfield.display_name | Mechanical Engineering |
| topics[1].display_name | Non-Destructive Testing Techniques |
| topics[2].id | https://openalex.org/T10534 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9945999979972839 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2205 |
| topics[2].subfield.display_name | Civil and Structural Engineering |
| topics[2].display_name | Structural Health Monitoring Techniques |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| funders[1].id | https://openalex.org/F4320321540 |
| funders[1].ror | https://ror.org/027s68j25 |
| funders[1].display_name | Ministry of Science and Technology of the People's Republic of China |
| funders[2].id | https://openalex.org/F4320322720 |
| funders[2].ror | https://ror.org/00hn7w693 |
| funders[2].display_name | Southwest Jiaotong University |
| funders[3].id | https://openalex.org/F4320322725 |
| funders[3].ror | https://ror.org/04atp4p48 |
| funders[3].display_name | China Scholarship Council |
| funders[4].id | https://openalex.org/F4320327029 |
| funders[4].ror | |
| funders[4].display_name | State Key Laboratory of Traction Power |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C33923547 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6695477962493896 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[0].display_name | Mathematics |
| concepts[1].id | https://openalex.org/C166963901 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5226606726646423 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q287251 |
| concepts[1].display_name | Kurtosis |
| concepts[2].id | https://openalex.org/C177148314 |
| concepts[2].level | 2 |
| concepts[2].score | 0.4880863428115845 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q170084 |
| concepts[2].display_name | Generalization |
| concepts[3].id | https://openalex.org/C118671147 |
| concepts[3].level | 2 |
| concepts[3].score | 0.4291396737098694 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q578714 |
| concepts[3].display_name | Quantile |
| concepts[4].id | https://openalex.org/C11413529 |
| concepts[4].level | 1 |
| concepts[4].score | 0.4275108575820923 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[4].display_name | Algorithm |
| concepts[5].id | https://openalex.org/C2780799671 |
| concepts[5].level | 2 |
| concepts[5].score | 0.415261447429657 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17087362 |
| concepts[5].display_name | Transient (computer programming) |
| concepts[6].id | https://openalex.org/C2780388253 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4109552800655365 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5421508 |
| concepts[6].display_name | Exponent |
| concepts[7].id | https://openalex.org/C105795698 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3747991919517517 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[7].display_name | Statistics |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.26990950107574463 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| concepts[9].id | https://openalex.org/C138885662 |
| concepts[9].level | 0 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[9].display_name | Philosophy |
| concepts[10].id | https://openalex.org/C41895202 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[10].display_name | Linguistics |
| concepts[11].id | https://openalex.org/C111919701 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[11].display_name | Operating system |
| concepts[12].id | https://openalex.org/C134306372 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[12].display_name | Mathematical analysis |
| keywords[0].id | https://openalex.org/keywords/mathematics |
| keywords[0].score | 0.6695477962493896 |
| keywords[0].display_name | Mathematics |
| keywords[1].id | https://openalex.org/keywords/kurtosis |
| keywords[1].score | 0.5226606726646423 |
| keywords[1].display_name | Kurtosis |
| keywords[2].id | https://openalex.org/keywords/generalization |
| keywords[2].score | 0.4880863428115845 |
| keywords[2].display_name | Generalization |
| keywords[3].id | https://openalex.org/keywords/quantile |
| keywords[3].score | 0.4291396737098694 |
| keywords[3].display_name | Quantile |
| keywords[4].id | https://openalex.org/keywords/algorithm |
| keywords[4].score | 0.4275108575820923 |
| keywords[4].display_name | Algorithm |
| keywords[5].id | https://openalex.org/keywords/transient |
| keywords[5].score | 0.415261447429657 |
| keywords[5].display_name | Transient (computer programming) |
| keywords[6].id | https://openalex.org/keywords/exponent |
| keywords[6].score | 0.4109552800655365 |
| keywords[6].display_name | Exponent |
| keywords[7].id | https://openalex.org/keywords/statistics |
| keywords[7].score | 0.3747991919517517 |
| keywords[7].display_name | Statistics |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.26990950107574463 |
| keywords[8].display_name | Computer science |
| language | en |
| locations[0].id | doi:10.1177/14759217221149745 |
| locations[0].is_oa | False |
| locations[0].source.id | https://openalex.org/S46950560 |
| locations[0].source.issn | 1475-9217, 1741-3168 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1475-9217 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Structural Health Monitoring |
| locations[0].source.host_organization | https://openalex.org/P4310320017 |
| locations[0].source.host_organization_name | SAGE Publishing |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320017 |
| locations[0].source.host_organization_lineage_names | SAGE Publishing |
| locations[0].license | |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Structural Health Monitoring |
| locations[0].landing_page_url | https://doi.org/10.1177/14759217221149745 |
| locations[1].id | pmh:oai:pure.atira.dk:publications/08ec33a9-f4f6-4c40-892c-13713fd0f4f7 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400216 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | Research Portal (King's College London) |
| locations[1].source.host_organization | https://openalex.org/I183935753 |
| locations[1].source.host_organization_name | King's College London |
| locations[1].source.host_organization_lineage | https://openalex.org/I183935753 |
| locations[1].license | other-oa |
| locations[1].pdf_url | https://pure.hud.ac.uk/ws/files/54564448/Manuscript_SHM_22_0384_Final_Version.pdf |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/other-oa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Chen, B, Gu, F, Zhang, W, Song, D, Cheng, Y & Zhou, Z 2023, 'Power function-based Gini indices : New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring', Structural Health Monitoring, vol. 22, no. 6, pp. 3677-3706. https://doi.org/10.1177/14759217221149745 |
| locations[1].landing_page_url | http://www.scopus.com/inward/record.url?scp=85149953307&partnerID=8YFLogxK |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5102984094 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7103-0221 |
| authorships[0].author.display_name | Bingyan Chen |
| authorships[0].countries | CN, GB |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I133837150 |
| authorships[0].affiliations[0].raw_affiliation_string | Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, UK |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I4800084 |
| authorships[0].affiliations[1].raw_affiliation_string | State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China |
| authorships[0].institutions[0].id | https://openalex.org/I4800084 |
| authorships[0].institutions[0].ror | https://ror.org/00hn7w693 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I4800084 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Southwest Jiaotong University |
| authorships[0].institutions[1].id | https://openalex.org/I133837150 |
| authorships[0].institutions[1].ror | https://ror.org/05t1h8f27 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I133837150 |
| authorships[0].institutions[1].country_code | GB |
| authorships[0].institutions[1].display_name | University of Huddersfield |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Bingyan Chen |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, UK, State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China |
| authorships[1].author.id | https://openalex.org/A5077515483 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-4907-525X |
| authorships[1].author.display_name | Fengshou Gu |
| authorships[1].countries | CN, GB |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I4800084 |
| authorships[1].affiliations[0].raw_affiliation_string | State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I133837150 |
| authorships[1].affiliations[1].raw_affiliation_string | Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, UK |
| authorships[1].institutions[0].id | https://openalex.org/I4800084 |
| authorships[1].institutions[0].ror | https://ror.org/00hn7w693 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I4800084 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Southwest Jiaotong University |
| authorships[1].institutions[1].id | https://openalex.org/I133837150 |
| authorships[1].institutions[1].ror | https://ror.org/05t1h8f27 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I133837150 |
| authorships[1].institutions[1].country_code | GB |
| authorships[1].institutions[1].display_name | University of Huddersfield |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Fengshou Gu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, UK, State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China |
| authorships[2].author.id | https://openalex.org/A5100370309 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3391-3872 |
| authorships[2].author.display_name | Weihua Zhang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I4800084 |
| authorships[2].affiliations[0].raw_affiliation_string | State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China |
| authorships[2].institutions[0].id | https://openalex.org/I4800084 |
| authorships[2].institutions[0].ror | https://ror.org/00hn7w693 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I4800084 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Southwest Jiaotong University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Weihua Zhang |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China |
| authorships[3].author.id | https://openalex.org/A5074669569 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8553-9480 |
| authorships[3].author.display_name | Dongli Song |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I4800084 |
| authorships[3].affiliations[0].raw_affiliation_string | State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China |
| authorships[3].institutions[0].id | https://openalex.org/I4800084 |
| authorships[3].institutions[0].ror | https://ror.org/00hn7w693 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I4800084 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Southwest Jiaotong University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Dongli Song |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China |
| authorships[4].author.id | https://openalex.org/A5002247841 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-2211-5971 |
| authorships[4].author.display_name | Yao Cheng |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I4800084 |
| authorships[4].affiliations[0].raw_affiliation_string | State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China |
| authorships[4].institutions[0].id | https://openalex.org/I4800084 |
| authorships[4].institutions[0].ror | https://ror.org/00hn7w693 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I4800084 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Southwest Jiaotong University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Yao Cheng |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu, China |
| authorships[5].author.id | https://openalex.org/A5051941856 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-3810-3676 |
| authorships[5].author.display_name | Zewen Zhou |
| authorships[5].countries | GB |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I133837150 |
| authorships[5].affiliations[0].raw_affiliation_string | Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, UK |
| authorships[5].institutions[0].id | https://openalex.org/I133837150 |
| authorships[5].institutions[0].ror | https://ror.org/05t1h8f27 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I133837150 |
| authorships[5].institutions[0].country_code | GB |
| authorships[5].institutions[0].display_name | University of Huddersfield |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Zewen Zhou |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield, UK |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://pure.hud.ac.uk/ws/files/54564448/Manuscript_SHM_22_0384_Final_Version.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Power function-based Gini indices: New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10220 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9995999932289124 |
| 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 | Machine Fault Diagnosis Techniques |
| related_works | https://openalex.org/W4381516319, https://openalex.org/W1506384729, https://openalex.org/W2037499216, https://openalex.org/W4225568567, https://openalex.org/W4286378979, https://openalex.org/W2075698830, https://openalex.org/W3127045225, https://openalex.org/W3216026256, https://openalex.org/W2059891554, https://openalex.org/W4235233670 |
| cited_by_count | 13 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 9 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:pure.atira.dk:publications/08ec33a9-f4f6-4c40-892c-13713fd0f4f7 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400216 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Research Portal (King's College London) |
| best_oa_location.source.host_organization | https://openalex.org/I183935753 |
| best_oa_location.source.host_organization_name | King's College London |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I183935753 |
| best_oa_location.license | other-oa |
| best_oa_location.pdf_url | https://pure.hud.ac.uk/ws/files/54564448/Manuscript_SHM_22_0384_Final_Version.pdf |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | article |
| best_oa_location.license_id | https://openalex.org/licenses/other-oa |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | Chen, B, Gu, F, Zhang, W, Song, D, Cheng, Y & Zhou, Z 2023, 'Power function-based Gini indices : New sparsity measures using power function-based quasi-arithmetic means for bearing condition monitoring', Structural Health Monitoring, vol. 22, no. 6, pp. 3677-3706. https://doi.org/10.1177/14759217221149745 |
| best_oa_location.landing_page_url | http://www.scopus.com/inward/record.url?scp=85149953307&partnerID=8YFLogxK |
| primary_location.id | doi:10.1177/14759217221149745 |
| primary_location.is_oa | False |
| primary_location.source.id | https://openalex.org/S46950560 |
| primary_location.source.issn | 1475-9217, 1741-3168 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1475-9217 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Structural Health Monitoring |
| primary_location.source.host_organization | https://openalex.org/P4310320017 |
| primary_location.source.host_organization_name | SAGE Publishing |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320017 |
| primary_location.source.host_organization_lineage_names | SAGE Publishing |
| primary_location.license | |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Structural Health Monitoring |
| primary_location.landing_page_url | https://doi.org/10.1177/14759217221149745 |
| publication_date | 2023-03-04 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2159357605, https://openalex.org/W2141386066, https://openalex.org/W2893488762, https://openalex.org/W3161130775, https://openalex.org/W4205353493, https://openalex.org/W1964511482, https://openalex.org/W2113860482, https://openalex.org/W1999891995, https://openalex.org/W2990543002, https://openalex.org/W3214433557, https://openalex.org/W2046674752, https://openalex.org/W2111854888, https://openalex.org/W1984516393, https://openalex.org/W2093849451, https://openalex.org/W2067376192, https://openalex.org/W2781219304, https://openalex.org/W2053347934, https://openalex.org/W427289305, https://openalex.org/W2751891979, https://openalex.org/W2793807982, https://openalex.org/W3011266123, https://openalex.org/W2970444935, https://openalex.org/W3193824608, https://openalex.org/W3016339222, https://openalex.org/W2019024593, https://openalex.org/W2033777252, https://openalex.org/W2757638459, https://openalex.org/W3122902015, https://openalex.org/W3000194924, https://openalex.org/W3194428826, https://openalex.org/W3179759556, https://openalex.org/W3134857068, https://openalex.org/W2053436766, https://openalex.org/W3006388671, https://openalex.org/W3158410226, https://openalex.org/W3198086451, https://openalex.org/W4225140325, https://openalex.org/W3110138466, https://openalex.org/W3187069023, https://openalex.org/W4200616694, https://openalex.org/W4281636184, https://openalex.org/W2317739826, https://openalex.org/W2022706486, https://openalex.org/W2019505419, https://openalex.org/W4225978065, https://openalex.org/W2904460913, https://openalex.org/W4281550753, https://openalex.org/W3093868050, https://openalex.org/W4210293859 |
| referenced_works_count | 49 |
| abstract_inverted_index.I | 72 |
| abstract_inverted_index.a | 139 |
| abstract_inverted_index.GI | 4, 7, 149, 152, 178, 181 |
| abstract_inverted_index.II | 150, 170, 179 |
| abstract_inverted_index.In | 133 |
| abstract_inverted_index.as | 31, 67, 81 |
| abstract_inverted_index.at | 192 |
| abstract_inverted_index.be | 12, 29 |
| abstract_inverted_index.by | 52, 77, 271 |
| abstract_inverted_index.in | 16 |
| abstract_inverted_index.is | 107, 146 |
| abstract_inverted_index.of | 19, 34, 62, 84, 129, 157, 195, 199, 207, 225, 250 |
| abstract_inverted_index.on | 137 |
| abstract_inverted_index.to | 11, 56, 65, 164, 215 |
| abstract_inverted_index.GI, | 57, 63 |
| abstract_inverted_index.II, | 5 |
| abstract_inverted_index.III | 8, 153, 172 |
| abstract_inverted_index.The | 0, 87, 247 |
| abstract_inverted_index.and | 6, 23, 26, 58, 111, 151, 171, 174, 180, 189, 202, 218, 229, 237, 257, 274 |
| abstract_inverted_index.are | 9, 203 |
| abstract_inverted_index.can | 28, 220, 230, 263, 275 |
| abstract_inverted_index.for | 49, 98, 143 |
| abstract_inverted_index.has | 74 |
| abstract_inverted_index.new | 205 |
| abstract_inverted_index.six | 196 |
| abstract_inverted_index.the | 17, 32, 82, 127, 130, 155, 158, 165, 223, 226, 266, 278, 284 |
| abstract_inverted_index.two | 204 |
| abstract_inverted_index.GGIs | 88 |
| abstract_inverted_index.Gini | 1, 43, 70, 168 |
| abstract_inverted_index.III, | 182 |
| abstract_inverted_index.been | 47, 75 |
| abstract_inverted_index.data | 227 |
| abstract_inverted_index.five | 194 |
| abstract_inverted_index.form | 61 |
| abstract_inverted_index.from | 154, 177 |
| abstract_inverted_index.have | 46 |
| abstract_inverted_index.here | 66 |
| abstract_inverted_index.high | 238 |
| abstract_inverted_index.into | 148 |
| abstract_inverted_index.mean | 160 |
| abstract_inverted_index.than | 109 |
| abstract_inverted_index.that | 187, 254 |
| abstract_inverted_index.they | 27 |
| abstract_inverted_index.this | 40, 134 |
| abstract_inverted_index.with | 89, 122, 243, 259, 283 |
| abstract_inverted_index.(GI), | 3 |
| abstract_inverted_index.PFGI1 | 116 |
| abstract_inverted_index.RQAM, | 138 |
| abstract_inverted_index.Under | 39 |
| abstract_inverted_index.based | 136 |
| abstract_inverted_index.fault | 24 |
| abstract_inverted_index.index | 2 |
| abstract_inverted_index.leads | 163 |
| abstract_inverted_index.least | 193 |
| abstract_inverted_index.lower | 108 |
| abstract_inverted_index.means | 37 |
| abstract_inverted_index.noise | 114 |
| abstract_inverted_index.power | 68, 79, 124, 166, 261 |
| abstract_inverted_index.ratio | 33 |
| abstract_inverted_index.shows | 212 |
| abstract_inverted_index.that, | 213 |
| abstract_inverted_index.their | 103 |
| abstract_inverted_index.under | 113 |
| abstract_inverted_index.using | 78 |
| abstract_inverted_index.which | 162 |
| abstract_inverted_index.while | 102 |
| abstract_inverted_index.(GGIs) | 45 |
| abstract_inverted_index.PFGI1s | 145 |
| abstract_inverted_index.PFGI2s | 188, 217 |
| abstract_inverted_index.PFGI3s | 190, 219, 258 |
| abstract_inverted_index.caused | 270 |
| abstract_inverted_index.faults | 273 |
| abstract_inverted_index.fields | 18 |
| abstract_inverted_index.means. | 86 |
| abstract_inverted_index.method | 142 |
| abstract_inverted_index.paper, | 135 |
| abstract_inverted_index.proven | 10 |
| abstract_inverted_index.proves | 186 |
| abstract_inverted_index.random | 234 |
| abstract_inverted_index.sparse | 50, 95 |
| abstract_inverted_index.status | 281 |
| abstract_inverted_index.strong | 233 |
| abstract_inverted_index.weight | 91 |
| abstract_inverted_index.(PFGI2s | 173 |
| abstract_inverted_index.(RQAM). | 38 |
| abstract_inverted_index.PFGI1s, | 216, 255 |
| abstract_inverted_index.PFGI2s, | 256 |
| abstract_inverted_index.PFGI3s) | 175 |
| abstract_inverted_index.achieve | 232 |
| abstract_inverted_index.another | 59 |
| abstract_inverted_index.bearing | 251, 272, 279 |
| abstract_inverted_index.exhibit | 93 |
| abstract_inverted_index.indices | 44, 71, 169 |
| abstract_inverted_index.machine | 20 |
| abstract_inverted_index.results | 249 |
| abstract_inverted_index.satisfy | 191 |
| abstract_inverted_index.showing | 126 |
| abstract_inverted_index.similar | 214 |
| abstract_inverted_index.typical | 197 |
| abstract_inverted_index.weights | 55 |
| abstract_inverted_index.achieves | 117 |
| abstract_inverted_index.analysis | 211 |
| abstract_inverted_index.applying | 53 |
| abstract_inverted_index.compared | 242, 282 |
| abstract_inverted_index.enhanced | 118 |
| abstract_inverted_index.estimate | 222 |
| abstract_inverted_index.families | 206 |
| abstract_inverted_index.features | 269 |
| abstract_inverted_index.function | 80 |
| abstract_inverted_index.kurtosis | 110 |
| abstract_inverted_index.measures | 15, 201 |
| abstract_inverted_index.quantify | 265 |
| abstract_inverted_index.referred | 64 |
| abstract_inverted_index.reliable | 94 |
| abstract_inverted_index.sequence | 228 |
| abstract_inverted_index.sparsity | 14, 200, 208, 224, 245, 286 |
| abstract_inverted_index.(PFGI1s), | 73 |
| abstract_inverted_index.advantage | 128 |
| abstract_inverted_index.approach. | 132 |
| abstract_inverted_index.condition | 21 |
| abstract_inverted_index.developed | 48 |
| abstract_inverted_index.different | 35, 90 |
| abstract_inverted_index.effective | 13 |
| abstract_inverted_index.exponent, | 125 |
| abstract_inverted_index.exponents | 262 |
| abstract_inverted_index.features, | 101 |
| abstract_inverted_index.generator | 83 |
| abstract_inverted_index.measures. | 209, 246, 287 |
| abstract_inverted_index.nonlinear | 54 |
| abstract_inverted_index.transient | 100, 105, 120, 235, 240, 268 |
| abstract_inverted_index.Simulation | 210 |
| abstract_inverted_index.accurately | 276 |
| abstract_inverted_index.attributes | 198 |
| abstract_inverted_index.capability | 97 |
| abstract_inverted_index.derivation | 185 |
| abstract_inverted_index.diagnosis, | 25 |
| abstract_inverted_index.framework, | 41 |
| abstract_inverted_index.generating | 144 |
| abstract_inverted_index.generator, | 161 |
| abstract_inverted_index.increasing | 123 |
| abstract_inverted_index.introduced | 76, 147 |
| abstract_inverted_index.monitoring | 22 |
| abstract_inverted_index.negentropy | 112 |
| abstract_inverted_index.parameters | 92 |
| abstract_inverted_index.repetitive | 99, 104, 119, 239, 267 |
| abstract_inverted_index.appropriate | 260 |
| abstract_inverted_index.constructed | 176 |
| abstract_inverted_index.degradation | 280 |
| abstract_inverted_index.demonstrate | 253 |
| abstract_inverted_index.effectively | 264 |
| abstract_inverted_index.generalized | 42, 60 |
| abstract_inverted_index.perspective | 156 |
| abstract_inverted_index.traditional | 244 |
| abstract_inverted_index.Mathematical | 184 |
| abstract_inverted_index.characterize | 277 |
| abstract_inverted_index.experimental | 248 |
| abstract_inverted_index.quantization | 96 |
| abstract_inverted_index.reformulated | 30 |
| abstract_inverted_index.monotonically | 221 |
| abstract_inverted_index.resistibility | 236 |
| abstract_inverted_index.respectively. | 183 |
| abstract_inverted_index.contamination. | 115 |
| abstract_inverted_index.function-based | 69, 167 |
| abstract_inverted_index.generalization | 131, 141 |
| abstract_inverted_index.quantification | 51 |
| abstract_inverted_index.run-to-failure | 252 |
| abstract_inverted_index.simultaneously | 231 |
| abstract_inverted_index.discriminability | 106, 121, 241 |
| abstract_inverted_index.quasi-arithmetic | 36, 85, 159 |
| abstract_inverted_index.single-parameter | 140 |
| abstract_inverted_index.state-of-the-art | 285 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5100370309 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I4800084 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.90429913 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |