Anomaly Detection Method for Harmonic Reducers with Only Healthy Data Article Swipe
Yuqing Li
,
Linghui Zhu
,
Minqiang Xu
,
Yunzhao Jia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24237435
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24237435
A harmonic reducer is an important component of industrial robots. In practical applications, it is difficult to obtain enough anomaly data from error cases for the supervised training of models. Whether the information contained in regular features is sensitive to anomaly detection is unknown. In this paper, we propose an anomaly detection frame for a harmonic reducer with only healthy data. We considered an auto-encoder trained using only healthy features, such as feature mapping, in which the difference between the output and the input constitutes a new high-dimensional feature space that retained information relevant only to anomalies. Compared to the original feature space, this space was more sensitive to abnormal data. The mapped features were then fed into the OCSVM to preserve the feature details of the abnormal information. The effectiveness of this method was validated by multiple sets of data collecting from harmonic reducers. Three different residual calculations and four different AE models were used, showing that the method outperforms an AE or an OCSVM alone. It is also verified that the method outperforms other typical anomaly detection methods.
Related Topics
Concepts
Anomaly detection
Reducer
Pattern recognition (psychology)
Computer science
Feature (linguistics)
Anomaly (physics)
Artificial intelligence
Residual
Harmonic
Feature vector
Feature extraction
Encoder
Frame (networking)
Data mining
Algorithm
Engineering
Physics
Philosophy
Telecommunications
Linguistics
Quantum mechanics
Condensed matter physics
Operating system
Civil engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24237435
- OA Status
- gold
- Cited By
- 1
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404593189
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4404593189Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s24237435Digital Object Identifier
- Title
-
Anomaly Detection Method for Harmonic Reducers with Only Healthy DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-21Full publication date if available
- Authors
-
Yuqing Li, Linghui Zhu, Minqiang Xu, Yunzhao JiaList of authors in order
- Landing page
-
https://doi.org/10.3390/s24237435Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.3390/s24237435Direct OA link when available
- Concepts
-
Anomaly detection, Reducer, Pattern recognition (psychology), Computer science, Feature (linguistics), Anomaly (physics), Artificial intelligence, Residual, Harmonic, Feature vector, Feature extraction, Encoder, Frame (networking), Data mining, Algorithm, Engineering, Physics, Philosophy, Telecommunications, Linguistics, Quantum mechanics, Condensed matter physics, Operating system, Civil engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4404593189 |
|---|---|
| doi | https://doi.org/10.3390/s24237435 |
| ids.doi | https://doi.org/10.3390/s24237435 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/39685970 |
| ids.openalex | https://openalex.org/W4404593189 |
| fwci | 0.63877855 |
| type | article |
| title | Anomaly Detection Method for Harmonic Reducers with Only Healthy Data |
| awards[0].id | https://openalex.org/G7074495934 |
| awards[0].funder_id | https://openalex.org/F4320335777 |
| awards[0].display_name | |
| awards[0].funder_award_id | 2021YFA1003501 |
| awards[0].funder_display_name | National Key Research and Development Program of China |
| awards[1].id | https://openalex.org/G8412275869 |
| awards[1].funder_id | https://openalex.org/F4320321001 |
| awards[1].display_name | |
| awards[1].funder_award_id | 52075117 |
| awards[1].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 23 |
| biblio.volume | 24 |
| biblio.last_page | 7435 |
| biblio.first_page | 7435 |
| topics[0].id | https://openalex.org/T11512 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9932000041007996 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1702 |
| topics[0].subfield.display_name | Artificial Intelligence |
| topics[0].display_name | Anomaly Detection Techniques and Applications |
| topics[1].id | https://openalex.org/T13429 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9897000193595886 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Electricity Theft Detection Techniques |
| topics[2].id | https://openalex.org/T12495 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9855999946594238 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2208 |
| topics[2].subfield.display_name | Electrical and Electronic Engineering |
| topics[2].display_name | Electrostatic Discharge in Electronics |
| 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/F4320335777 |
| funders[1].ror | |
| funders[1].display_name | National Key Research and Development Program of China |
| is_xpac | False |
| apc_list.value | 2400 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2598 |
| apc_paid.value | 2400 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2598 |
| concepts[0].id | https://openalex.org/C739882 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7526488304138184 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q3560506 |
| concepts[0].display_name | Anomaly detection |
| concepts[1].id | https://openalex.org/C2776985865 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7123666405677795 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q26820931 |
| concepts[1].display_name | Reducer |
| concepts[2].id | https://openalex.org/C153180895 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6537016034126282 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[2].display_name | Pattern recognition (psychology) |
| concepts[3].id | https://openalex.org/C41008148 |
| concepts[3].level | 0 |
| concepts[3].score | 0.6164193153381348 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[3].display_name | Computer science |
| concepts[4].id | https://openalex.org/C2776401178 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6120977401733398 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[4].display_name | Feature (linguistics) |
| concepts[5].id | https://openalex.org/C12997251 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5944323539733887 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q567560 |
| concepts[5].display_name | Anomaly (physics) |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.5809207558631897 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C155512373 |
| concepts[7].level | 2 |
| concepts[7].score | 0.5231843590736389 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q287450 |
| concepts[7].display_name | Residual |
| concepts[8].id | https://openalex.org/C127934551 |
| concepts[8].level | 2 |
| concepts[8].score | 0.5212069749832153 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1148098 |
| concepts[8].display_name | Harmonic |
| concepts[9].id | https://openalex.org/C83665646 |
| concepts[9].level | 2 |
| concepts[9].score | 0.5047906637191772 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q42139305 |
| concepts[9].display_name | Feature vector |
| concepts[10].id | https://openalex.org/C52622490 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4647148847579956 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1026626 |
| concepts[10].display_name | Feature extraction |
| concepts[11].id | https://openalex.org/C118505674 |
| concepts[11].level | 2 |
| concepts[11].score | 0.46115919947624207 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q42586063 |
| concepts[11].display_name | Encoder |
| concepts[12].id | https://openalex.org/C126042441 |
| concepts[12].level | 2 |
| concepts[12].score | 0.4318467080593109 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1324888 |
| concepts[12].display_name | Frame (networking) |
| concepts[13].id | https://openalex.org/C124101348 |
| concepts[13].level | 1 |
| concepts[13].score | 0.337358295917511 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[13].display_name | Data mining |
| concepts[14].id | https://openalex.org/C11413529 |
| concepts[14].level | 1 |
| concepts[14].score | 0.20007234811782837 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[14].display_name | Algorithm |
| concepts[15].id | https://openalex.org/C127413603 |
| concepts[15].level | 0 |
| concepts[15].score | 0.14420557022094727 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[15].display_name | Engineering |
| concepts[16].id | https://openalex.org/C121332964 |
| concepts[16].level | 0 |
| concepts[16].score | 0.060403972864151 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[16].display_name | Physics |
| concepts[17].id | https://openalex.org/C138885662 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[17].display_name | Philosophy |
| concepts[18].id | https://openalex.org/C76155785 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[18].display_name | Telecommunications |
| concepts[19].id | https://openalex.org/C41895202 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[19].display_name | Linguistics |
| concepts[20].id | https://openalex.org/C62520636 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[20].display_name | Quantum mechanics |
| concepts[21].id | https://openalex.org/C26873012 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q214781 |
| concepts[21].display_name | Condensed matter physics |
| concepts[22].id | https://openalex.org/C111919701 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q9135 |
| concepts[22].display_name | Operating system |
| concepts[23].id | https://openalex.org/C147176958 |
| concepts[23].level | 1 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q77590 |
| concepts[23].display_name | Civil engineering |
| keywords[0].id | https://openalex.org/keywords/anomaly-detection |
| keywords[0].score | 0.7526488304138184 |
| keywords[0].display_name | Anomaly detection |
| keywords[1].id | https://openalex.org/keywords/reducer |
| keywords[1].score | 0.7123666405677795 |
| keywords[1].display_name | Reducer |
| keywords[2].id | https://openalex.org/keywords/pattern-recognition |
| keywords[2].score | 0.6537016034126282 |
| keywords[2].display_name | Pattern recognition (psychology) |
| keywords[3].id | https://openalex.org/keywords/computer-science |
| keywords[3].score | 0.6164193153381348 |
| keywords[3].display_name | Computer science |
| keywords[4].id | https://openalex.org/keywords/feature |
| keywords[4].score | 0.6120977401733398 |
| keywords[4].display_name | Feature (linguistics) |
| keywords[5].id | https://openalex.org/keywords/anomaly |
| keywords[5].score | 0.5944323539733887 |
| keywords[5].display_name | Anomaly (physics) |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.5809207558631897 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/residual |
| keywords[7].score | 0.5231843590736389 |
| keywords[7].display_name | Residual |
| keywords[8].id | https://openalex.org/keywords/harmonic |
| keywords[8].score | 0.5212069749832153 |
| keywords[8].display_name | Harmonic |
| keywords[9].id | https://openalex.org/keywords/feature-vector |
| keywords[9].score | 0.5047906637191772 |
| keywords[9].display_name | Feature vector |
| keywords[10].id | https://openalex.org/keywords/feature-extraction |
| keywords[10].score | 0.4647148847579956 |
| keywords[10].display_name | Feature extraction |
| keywords[11].id | https://openalex.org/keywords/encoder |
| keywords[11].score | 0.46115919947624207 |
| keywords[11].display_name | Encoder |
| keywords[12].id | https://openalex.org/keywords/frame |
| keywords[12].score | 0.4318467080593109 |
| keywords[12].display_name | Frame (networking) |
| keywords[13].id | https://openalex.org/keywords/data-mining |
| keywords[13].score | 0.337358295917511 |
| keywords[13].display_name | Data mining |
| keywords[14].id | https://openalex.org/keywords/algorithm |
| keywords[14].score | 0.20007234811782837 |
| keywords[14].display_name | Algorithm |
| keywords[15].id | https://openalex.org/keywords/engineering |
| keywords[15].score | 0.14420557022094727 |
| keywords[15].display_name | Engineering |
| keywords[16].id | https://openalex.org/keywords/physics |
| keywords[16].score | 0.060403972864151 |
| keywords[16].display_name | Physics |
| language | en |
| locations[0].id | doi:10.3390/s24237435 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S101949793 |
| locations[0].source.issn | 1424-8220 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1424-8220 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Sensors |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Sensors |
| locations[0].landing_page_url | https://doi.org/10.3390/s24237435 |
| locations[1].id | pmid:39685970 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| 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 | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Sensors (Basel, Switzerland) |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/39685970 |
| locations[2].id | pmh:oai:doaj.org/article:8515918a281145d2beca2aec985ef59b |
| locations[2].is_oa | False |
| locations[2].source.id | https://openalex.org/S4306401280 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[2].source.host_organization | |
| locations[2].source.host_organization_name | |
| locations[2].license | |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | article |
| locations[2].license_id | |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Sensors, Vol 24, Iss 23, p 7435 (2024) |
| locations[2].landing_page_url | https://doaj.org/article/8515918a281145d2beca2aec985ef59b |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5100379931 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-0275-4004 |
| authorships[0].author.display_name | Yuqing Li |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I204983213 |
| authorships[0].affiliations[0].raw_affiliation_string | Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China |
| authorships[0].institutions[0].id | https://openalex.org/I204983213 |
| authorships[0].institutions[0].ror | https://ror.org/01yqg2h08 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I204983213 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Harbin Institute of Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yuqing Li |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China |
| authorships[1].author.id | https://openalex.org/A5101824364 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6476-7943 |
| authorships[1].author.display_name | Linghui Zhu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I204983213 |
| authorships[1].affiliations[0].raw_affiliation_string | Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China |
| authorships[1].institutions[0].id | https://openalex.org/I204983213 |
| authorships[1].institutions[0].ror | https://ror.org/01yqg2h08 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I204983213 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Harbin Institute of Technology |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Linghui Zhu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China |
| authorships[2].author.id | https://openalex.org/A5100413867 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3625-1736 |
| authorships[2].author.display_name | Minqiang Xu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I204983213 |
| authorships[2].affiliations[0].raw_affiliation_string | Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China |
| authorships[2].institutions[0].id | https://openalex.org/I204983213 |
| authorships[2].institutions[0].ror | https://ror.org/01yqg2h08 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I204983213 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Harbin Institute of Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Minqiang Xu |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China |
| authorships[3].author.id | https://openalex.org/A5019076984 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-3862-6987 |
| authorships[3].author.display_name | Yunzhao Jia |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I204983213 |
| authorships[3].affiliations[0].raw_affiliation_string | Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China |
| authorships[3].institutions[0].id | https://openalex.org/I204983213 |
| authorships[3].institutions[0].ror | https://ror.org/01yqg2h08 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I204983213 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Harbin Institute of Technology |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Yunzhao Jia |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin 150001, China |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.3390/s24237435 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Anomaly Detection Method for Harmonic Reducers with Only Healthy Data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11512 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9932000041007996 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1702 |
| primary_topic.subfield.display_name | Artificial Intelligence |
| primary_topic.display_name | Anomaly Detection Techniques and Applications |
| related_works | https://openalex.org/W2806741695, https://openalex.org/W4290647774, https://openalex.org/W3189286258, https://openalex.org/W3207797160, https://openalex.org/W3210364259, https://openalex.org/W4300558037, https://openalex.org/W2667207928, https://openalex.org/W2912112202, https://openalex.org/W4377864969, https://openalex.org/W3120251014 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/s24237435 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S101949793 |
| best_oa_location.source.issn | 1424-8220 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1424-8220 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Sensors |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Sensors |
| best_oa_location.landing_page_url | https://doi.org/10.3390/s24237435 |
| primary_location.id | doi:10.3390/s24237435 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S101949793 |
| primary_location.source.issn | 1424-8220 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1424-8220 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Sensors |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Sensors |
| primary_location.landing_page_url | https://doi.org/10.3390/s24237435 |
| publication_date | 2024-11-21 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4206166171, https://openalex.org/W2545188963, https://openalex.org/W4391146167, https://openalex.org/W6851551972, https://openalex.org/W3098362015, https://openalex.org/W4391672468, https://openalex.org/W6853716572, https://openalex.org/W4206199419, https://openalex.org/W3164952570, https://openalex.org/W3186896732, https://openalex.org/W4295308215, https://openalex.org/W2999299646, https://openalex.org/W4392904636, https://openalex.org/W3040266635, https://openalex.org/W4312181495, https://openalex.org/W4306929565, https://openalex.org/W4391407122, https://openalex.org/W4308906783, https://openalex.org/W4221134235, https://openalex.org/W4390702160, https://openalex.org/W4200012855, https://openalex.org/W3092704883, https://openalex.org/W2964300371, https://openalex.org/W2944603198, https://openalex.org/W4294975729, https://openalex.org/W3103648030, https://openalex.org/W4382204515, https://openalex.org/W976179410, https://openalex.org/W4283072848, https://openalex.org/W3084709735, https://openalex.org/W3127669014, https://openalex.org/W3023212902, https://openalex.org/W3214066484, https://openalex.org/W2942497026, https://openalex.org/W2910992412, https://openalex.org/W6754841127, https://openalex.org/W2789758093, https://openalex.org/W2902834302, https://openalex.org/W3133003026, https://openalex.org/W3103897629, https://openalex.org/W3117098906, https://openalex.org/W4362603404, https://openalex.org/W4382998598, https://openalex.org/W2963307331, https://openalex.org/W3135550350 |
| referenced_works_count | 45 |
| abstract_inverted_index.A | 0 |
| abstract_inverted_index.a | 54, 85 |
| abstract_inverted_index.AE | 152, 162 |
| abstract_inverted_index.In | 10, 44 |
| abstract_inverted_index.It | 167 |
| abstract_inverted_index.We | 61 |
| abstract_inverted_index.an | 4, 49, 63, 161, 164 |
| abstract_inverted_index.as | 71 |
| abstract_inverted_index.by | 136 |
| abstract_inverted_index.in | 34, 74 |
| abstract_inverted_index.is | 3, 14, 37, 42, 168 |
| abstract_inverted_index.it | 13 |
| abstract_inverted_index.of | 7, 28, 125, 131, 139 |
| abstract_inverted_index.or | 163 |
| abstract_inverted_index.to | 16, 39, 95, 98, 108, 120 |
| abstract_inverted_index.we | 47 |
| abstract_inverted_index.The | 111, 129 |
| abstract_inverted_index.and | 81, 149 |
| abstract_inverted_index.fed | 116 |
| abstract_inverted_index.for | 24, 53 |
| abstract_inverted_index.new | 86 |
| abstract_inverted_index.the | 25, 31, 76, 79, 82, 99, 118, 122, 126, 158, 172 |
| abstract_inverted_index.was | 105, 134 |
| abstract_inverted_index.also | 169 |
| abstract_inverted_index.data | 20, 140 |
| abstract_inverted_index.four | 150 |
| abstract_inverted_index.from | 21, 142 |
| abstract_inverted_index.into | 117 |
| abstract_inverted_index.more | 106 |
| abstract_inverted_index.only | 58, 67, 94 |
| abstract_inverted_index.sets | 138 |
| abstract_inverted_index.such | 70 |
| abstract_inverted_index.that | 90, 157, 171 |
| abstract_inverted_index.then | 115 |
| abstract_inverted_index.this | 45, 103, 132 |
| abstract_inverted_index.were | 114, 154 |
| abstract_inverted_index.with | 57 |
| abstract_inverted_index.OCSVM | 119, 165 |
| abstract_inverted_index.Three | 145 |
| abstract_inverted_index.cases | 23 |
| abstract_inverted_index.data. | 60, 110 |
| abstract_inverted_index.error | 22 |
| abstract_inverted_index.frame | 52 |
| abstract_inverted_index.input | 83 |
| abstract_inverted_index.other | 175 |
| abstract_inverted_index.space | 89, 104 |
| abstract_inverted_index.used, | 155 |
| abstract_inverted_index.using | 66 |
| abstract_inverted_index.which | 75 |
| abstract_inverted_index.alone. | 166 |
| abstract_inverted_index.enough | 18 |
| abstract_inverted_index.mapped | 112 |
| abstract_inverted_index.method | 133, 159, 173 |
| abstract_inverted_index.models | 153 |
| abstract_inverted_index.obtain | 17 |
| abstract_inverted_index.output | 80 |
| abstract_inverted_index.paper, | 46 |
| abstract_inverted_index.space, | 102 |
| abstract_inverted_index.Whether | 30 |
| abstract_inverted_index.anomaly | 19, 40, 50, 177 |
| abstract_inverted_index.between | 78 |
| abstract_inverted_index.details | 124 |
| abstract_inverted_index.feature | 72, 88, 101, 123 |
| abstract_inverted_index.healthy | 59, 68 |
| abstract_inverted_index.models. | 29 |
| abstract_inverted_index.propose | 48 |
| abstract_inverted_index.reducer | 2, 56 |
| abstract_inverted_index.regular | 35 |
| abstract_inverted_index.robots. | 9 |
| abstract_inverted_index.showing | 156 |
| abstract_inverted_index.trained | 65 |
| abstract_inverted_index.typical | 176 |
| abstract_inverted_index.Compared | 97 |
| abstract_inverted_index.abnormal | 109, 127 |
| abstract_inverted_index.features | 36, 113 |
| abstract_inverted_index.harmonic | 1, 55, 143 |
| abstract_inverted_index.mapping, | 73 |
| abstract_inverted_index.methods. | 179 |
| abstract_inverted_index.multiple | 137 |
| abstract_inverted_index.original | 100 |
| abstract_inverted_index.preserve | 121 |
| abstract_inverted_index.relevant | 93 |
| abstract_inverted_index.residual | 147 |
| abstract_inverted_index.retained | 91 |
| abstract_inverted_index.training | 27 |
| abstract_inverted_index.unknown. | 43 |
| abstract_inverted_index.verified | 170 |
| abstract_inverted_index.component | 6 |
| abstract_inverted_index.contained | 33 |
| abstract_inverted_index.detection | 41, 51, 178 |
| abstract_inverted_index.different | 146, 151 |
| abstract_inverted_index.difficult | 15 |
| abstract_inverted_index.features, | 69 |
| abstract_inverted_index.important | 5 |
| abstract_inverted_index.practical | 11 |
| abstract_inverted_index.reducers. | 144 |
| abstract_inverted_index.sensitive | 38, 107 |
| abstract_inverted_index.validated | 135 |
| abstract_inverted_index.anomalies. | 96 |
| abstract_inverted_index.collecting | 141 |
| abstract_inverted_index.considered | 62 |
| abstract_inverted_index.difference | 77 |
| abstract_inverted_index.industrial | 8 |
| abstract_inverted_index.supervised | 26 |
| abstract_inverted_index.constitutes | 84 |
| abstract_inverted_index.information | 32, 92 |
| abstract_inverted_index.outperforms | 160, 174 |
| abstract_inverted_index.auto-encoder | 64 |
| abstract_inverted_index.calculations | 148 |
| abstract_inverted_index.information. | 128 |
| abstract_inverted_index.applications, | 12 |
| abstract_inverted_index.effectiveness | 130 |
| abstract_inverted_index.high-dimensional | 87 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5100413867 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I204983213 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.73004913 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |