The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study
• A practical guideline on leveraging graph neural networks (GNNs) for realizing intelligent fault diagnostics and prognostics. • A novel intelligent fault diagnostics and prognostics framework based on GNNs is established to illustrate how the proposed guideline works. • In this framework, three ty...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Tianfu [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
Intelligent fault diagnostics and prognostics |
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| Übergeordnetes Werk: |
Enthalten in: Species loss from land use of oil palm plantations in Thailand - Jaroenkietkajorn, Ukrit ELSEVIER, 2021, mssp, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:168 ; year:2022 ; day:1 ; month:04 ; pages:0 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ymssp.2021.108653 |
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| 520 | |a • A practical guideline on leveraging graph neural networks (GNNs) for realizing intelligent fault diagnostics and prognostics. • A novel intelligent fault diagnostics and prognostics framework based on GNNs is established to illustrate how the proposed guideline works. • In this framework, three types graph construction methods are provided, and seven kinds of graph convolutional networks with four graph pooling methods are investigated. • A benchmark study of these models is carried out on eight datasets, including six fault diagnosis datasets and two prognosis datasets. | ||
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10.1016/j.ymssp.2021.108653 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001652.pica (DE-627)ELV056518927 (ELSEVIER)S0888-3270(21)00979-1 DE-627 ger DE-627 rakwb eng 570 630 VZ BIODIV DE-30 fid Li, Tianfu verfasserin aut The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A practical guideline on leveraging graph neural networks (GNNs) for realizing intelligent fault diagnostics and prognostics. • A novel intelligent fault diagnostics and prognostics framework based on GNNs is established to illustrate how the proposed guideline works. • In this framework, three types graph construction methods are provided, and seven kinds of graph convolutional networks with four graph pooling methods are investigated. • A benchmark study of these models is carried out on eight datasets, including six fault diagnosis datasets and two prognosis datasets. Intelligent fault diagnostics and prognostics Elsevier Graph neural networks Elsevier Benchmark results Elsevier Prognostics and health management Elsevier Practical guideline Elsevier Zhou, Zheng oth Li, Sinan oth Sun, Chuang oth Yan, Ruqiang oth Chen, Xuefeng oth Enthalten in Elsevier Jaroenkietkajorn, Ukrit ELSEVIER Species loss from land use of oil palm plantations in Thailand 2021 mssp Amsterdam [u.a.] (DE-627)ELV007151810 volume:168 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.ymssp.2021.108653 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA AR 168 2022 1 0401 0 |
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10.1016/j.ymssp.2021.108653 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001652.pica (DE-627)ELV056518927 (ELSEVIER)S0888-3270(21)00979-1 DE-627 ger DE-627 rakwb eng 570 630 VZ BIODIV DE-30 fid Li, Tianfu verfasserin aut The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study 2022 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • A practical guideline on leveraging graph neural networks (GNNs) for realizing intelligent fault diagnostics and prognostics. • A novel intelligent fault diagnostics and prognostics framework based on GNNs is established to illustrate how the proposed guideline works. • In this framework, three types graph construction methods are provided, and seven kinds of graph convolutional networks with four graph pooling methods are investigated. • A benchmark study of these models is carried out on eight datasets, including six fault diagnosis datasets and two prognosis datasets. Intelligent fault diagnostics and prognostics Elsevier Graph neural networks Elsevier Benchmark results Elsevier Prognostics and health management Elsevier Practical guideline Elsevier Zhou, Zheng oth Li, Sinan oth Sun, Chuang oth Yan, Ruqiang oth Chen, Xuefeng oth Enthalten in Elsevier Jaroenkietkajorn, Ukrit ELSEVIER Species loss from land use of oil palm plantations in Thailand 2021 mssp Amsterdam [u.a.] (DE-627)ELV007151810 volume:168 year:2022 day:1 month:04 pages:0 https://doi.org/10.1016/j.ymssp.2021.108653 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA AR 168 2022 1 0401 0 |
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• A practical guideline on leveraging graph neural networks (GNNs) for realizing intelligent fault diagnostics and prognostics. • A novel intelligent fault diagnostics and prognostics framework based on GNNs is established to illustrate how the proposed guideline works. • In this framework, three types graph construction methods are provided, and seven kinds of graph convolutional networks with four graph pooling methods are investigated. • A benchmark study of these models is carried out on eight datasets, including six fault diagnosis datasets and two prognosis datasets. |
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• A practical guideline on leveraging graph neural networks (GNNs) for realizing intelligent fault diagnostics and prognostics. • A novel intelligent fault diagnostics and prognostics framework based on GNNs is established to illustrate how the proposed guideline works. • In this framework, three types graph construction methods are provided, and seven kinds of graph convolutional networks with four graph pooling methods are investigated. • A benchmark study of these models is carried out on eight datasets, including six fault diagnosis datasets and two prognosis datasets. |
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• A practical guideline on leveraging graph neural networks (GNNs) for realizing intelligent fault diagnostics and prognostics. • A novel intelligent fault diagnostics and prognostics framework based on GNNs is established to illustrate how the proposed guideline works. • In this framework, three types graph construction methods are provided, and seven kinds of graph convolutional networks with four graph pooling methods are investigated. • A benchmark study of these models is carried out on eight datasets, including six fault diagnosis datasets and two prognosis datasets. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV056518927</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624232524.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220205s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.ymssp.2021.108653</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001652.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV056518927</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0888-3270(21)00979-1</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield 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ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">• A practical guideline on leveraging graph neural networks (GNNs) for realizing intelligent fault diagnostics and prognostics. • A novel intelligent fault diagnostics and prognostics framework based on GNNs is established to illustrate how the proposed guideline works. • In this framework, three types graph construction methods are provided, and seven kinds of graph convolutional networks with four graph pooling methods are investigated. • A 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