Sparse canonical variate analysis approach for process monitoring
• Sparse canonical variate analysis (SCVA) is proposed for process monitoring. • SCVA applies to a broader set of datasets than canonical variate analysis. • SCVA is even applicable for singular covariance matrices and small sample sizes. • SCVA facilitates the discovery of major relationships among...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Lu, Qiugang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Umfang: |
13 |
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| Übergeordnetes Werk: |
Enthalten in: A metric to gauge local distortion in metallic glasses and supercooled liquids - Wu, Chen ELSEVIER, 2014transfer abstract, a journal affiliated with IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:71 ; year:2018 ; pages:90-102 ; extent:13 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.jprocont.2018.09.009 |
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ELV044914040 |
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| 520 | |a • Sparse canonical variate analysis (SCVA) is proposed for process monitoring. • SCVA applies to a broader set of datasets than canonical variate analysis. • SCVA is even applicable for singular covariance matrices and small sample sizes. • SCVA facilitates the discovery of major relationships among the process variables. • Effectiveness for process monitoring is demonstrated in a realistic case study. | ||
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| 650 | 7 | |a Fault detection and identification |2 Elsevier | |
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10.1016/j.jprocont.2018.09.009 doi GBV00000000000425.pica (DE-627)ELV044914040 (ELSEVIER)S0959-1524(18)30345-7 DE-627 ger DE-627 rakwb eng 670 VZ 330 VZ Lu, Qiugang verfasserin aut Sparse canonical variate analysis approach for process monitoring 2018 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Sparse canonical variate analysis (SCVA) is proposed for process monitoring. • SCVA applies to a broader set of datasets than canonical variate analysis. • SCVA is even applicable for singular covariance matrices and small sample sizes. • SCVA facilitates the discovery of major relationships among the process variables. • Effectiveness for process monitoring is demonstrated in a realistic case study. Process monitoring Elsevier Fault detection and identification Elsevier Tennessee Eastman process Elsevier Canonical variate analysis Elsevier Contribution plot Elsevier Jiang, Benben oth Gopaluni, R. Bhushan oth Loewen, Philip D. oth Braatz, Richard D. oth Enthalten in Elsevier Science Wu, Chen ELSEVIER A metric to gauge local distortion in metallic glasses and supercooled liquids 2014transfer abstract a journal affiliated with IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV022993630 volume:71 year:2018 pages:90-102 extent:13 https://doi.org/10.1016/j.jprocont.2018.09.009 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 GBV_ILN_73 AR 71 2018 90-102 13 |
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• Sparse canonical variate analysis (SCVA) is proposed for process monitoring. • SCVA applies to a broader set of datasets than canonical variate analysis. • SCVA is even applicable for singular covariance matrices and small sample sizes. • SCVA facilitates the discovery of major relationships among the process variables. • Effectiveness for process monitoring is demonstrated in a realistic case study. |
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• Sparse canonical variate analysis (SCVA) is proposed for process monitoring. • SCVA applies to a broader set of datasets than canonical variate analysis. • SCVA is even applicable for singular covariance matrices and small sample sizes. • SCVA facilitates the discovery of major relationships among the process variables. • Effectiveness for process monitoring is demonstrated in a realistic case study. |
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• Sparse canonical variate analysis (SCVA) is proposed for process monitoring. • SCVA applies to a broader set of datasets than canonical variate analysis. • SCVA is even applicable for singular covariance matrices and small sample sizes. • SCVA facilitates the discovery of major relationships among the process variables. • Effectiveness for process monitoring is demonstrated in a realistic case study. |
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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">ELV044914040</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230624112846.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">181123s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.jprocont.2018.09.009</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBV00000000000425.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV044914040</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0959-1524(18)30345-7</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">670</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">330</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lu, Qiugang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Sparse canonical variate analysis approach for process monitoring</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">13</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">• Sparse canonical variate analysis (SCVA) is proposed for process monitoring. • SCVA applies to a broader set of datasets than canonical variate analysis. • SCVA is even applicable for singular covariance matrices and small sample sizes. • SCVA facilitates the discovery of major relationships among the process variables. • Effectiveness for process monitoring is demonstrated in a realistic case study.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Process monitoring</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Fault detection and identification</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Tennessee Eastman process</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Canonical variate analysis</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Contribution plot</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jiang, Benben</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gopaluni, R. 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