Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet
• The architecture of event-driven tool condition monitoring (EDTCM) based on Industrial Internet is proposed. • The "just-in-time" TCM is realized by triggering "monitoring" events when the workpiece starts machining. • Event processing technology considering multi-source data i...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Yahui [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Umfang: |
18 |
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| Übergeordnetes Werk: |
Enthalten in: Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota - Bayulgen, Oksan ELSEVIER, 2021, Dearborn, Mich |
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| Übergeordnetes Werk: |
volume:58 ; year:2021 ; pages:205-222 ; extent:18 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jmsy.2020.11.019 |
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ELV053088387 |
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| 520 | |a • The architecture of event-driven tool condition monitoring (EDTCM) based on Industrial Internet is proposed. • The "just-in-time" TCM is realized by triggering "monitoring" events when the workpiece starts machining. • Event processing technology considering multi-source data is proposed to analyze the workpiece machining states. • The Bayesian method is proposed for on-line updating the prediction of remaining useful life of the tool. • A prototype system is developed and deployed in the workshop production site. | ||
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10.1016/j.jmsy.2020.11.019 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001296.pica (DE-627)ELV053088387 (ELSEVIER)S0278-6125(20)30213-2 DE-627 ger DE-627 rakwb eng 620 VZ 83.65 bkl Wang, Yahui verfasserin aut Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet 2021 18 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • The architecture of event-driven tool condition monitoring (EDTCM) based on Industrial Internet is proposed. • The "just-in-time" TCM is realized by triggering "monitoring" events when the workpiece starts machining. • Event processing technology considering multi-source data is proposed to analyze the workpiece machining states. • The Bayesian method is proposed for on-line updating the prediction of remaining useful life of the tool. • A prototype system is developed and deployed in the workshop production site. OPC-UA Elsevier Industrial internet Elsevier Tool life prediction Elsevier Event-driven Elsevier Tool condition monitoring Elsevier Zheng, Lianyu oth Wang, Yiwei oth Enthalten in Soc Bayulgen, Oksan ELSEVIER Tilting at windmills? Electoral repercussions of wind turbine projects in Minnesota 2021 Dearborn, Mich (DE-627)ELV00685088X volume:58 year:2021 pages:205-222 extent:18 https://doi.org/10.1016/j.jmsy.2020.11.019 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 83.65 Versorgungswirtschaft VZ AR 58 2021 205-222 18 |
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• The architecture of event-driven tool condition monitoring (EDTCM) based on Industrial Internet is proposed. • The "just-in-time" TCM is realized by triggering "monitoring" events when the workpiece starts machining. • Event processing technology considering multi-source data is proposed to analyze the workpiece machining states. • The Bayesian method is proposed for on-line updating the prediction of remaining useful life of the tool. • A prototype system is developed and deployed in the workshop production site. |
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• The architecture of event-driven tool condition monitoring (EDTCM) based on Industrial Internet is proposed. • The "just-in-time" TCM is realized by triggering "monitoring" events when the workpiece starts machining. • Event processing technology considering multi-source data is proposed to analyze the workpiece machining states. • The Bayesian method is proposed for on-line updating the prediction of remaining useful life of the tool. • A prototype system is developed and deployed in the workshop production site. |
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• The architecture of event-driven tool condition monitoring (EDTCM) based on Industrial Internet is proposed. • The "just-in-time" TCM is realized by triggering "monitoring" events when the workpiece starts machining. • Event processing technology considering multi-source data is proposed to analyze the workpiece machining states. • The Bayesian method is proposed for on-line updating the prediction of remaining useful life of the tool. • A prototype system is developed and deployed in the workshop production site. |
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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">ELV053088387</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624191945.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2021 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jmsy.2020.11.019</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/GBV00000000001296.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV053088387</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0278-6125(20)30213-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">83.65</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Yahui</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">18</subfield></datafield><datafield tag="336" 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">• The architecture of event-driven tool condition monitoring (EDTCM) based on Industrial Internet is proposed. • The "just-in-time" TCM is realized by triggering "monitoring" events when the workpiece starts machining. • Event processing technology considering multi-source data is proposed to analyze the workpiece machining states. • The Bayesian method is proposed for on-line updating the prediction of remaining useful life of the tool. • A prototype system is developed and deployed in the workshop production site.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">OPC-UA</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Industrial internet</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Tool life prediction</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Event-driven</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Tool condition monitoring</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zheng, Lianyu</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yiwei</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Soc</subfield><subfield code="a">Bayulgen, Oksan ELSEVIER</subfield><subfield code="t">Tilting at windmills? 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