Effects of Model Accuracy on Residual Control Charts
Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is...
Ausführliche Beschreibung
Autor*in: |
Zhou, Min [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2016 |
---|
Rechteinformationen: |
Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. |
---|
Schlagwörter: |
---|
Übergeordnetes Werk: |
Enthalten in: Quality and reliability engineering international - Chichester [u.a.] : Wiley, 1985, 32(2016), 5, Seite 1785-1794 |
---|---|
Übergeordnetes Werk: |
volume:32 ; year:2016 ; number:5 ; pages:1785-1794 |
Links: |
---|
DOI / URN: |
10.1002/qre.1913 |
---|
Katalog-ID: |
OLC1979247501 |
---|
LEADER | 01000caa a2200265 4500 | ||
---|---|---|---|
001 | OLC1979247501 | ||
003 | DE-627 | ||
005 | 20220217093006.0 | ||
007 | tu | ||
008 | 160720s2016 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1002/qre.1913 |2 doi | |
028 | 5 | 2 | |a PQ20160720 |
035 | |a (DE-627)OLC1979247501 | ||
035 | |a (DE-599)GBVOLC1979247501 | ||
035 | |a (PRQ)p1193-5ba87fd202171378d392e1cca01f8b61365a39d92eeabd2ff278e2a886647853 | ||
035 | |a (KEY)0136540120160000032000501785effectsofmodelaccuracyonresidualcontrolcharts | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 650 |a 690 |q DNB |
084 | |a 50.16 |2 bkl | ||
084 | |a 85.38 |2 bkl | ||
100 | 1 | |a Zhou, Min |e verfasserin |4 aut | |
245 | 1 | 0 | |a Effects of Model Accuracy on Residual Control Charts |
264 | 1 | |c 2016 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
520 | |a Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd. | ||
540 | |a Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. | ||
650 | 4 | |a average run length | |
650 | 4 | |a multivariate autogressive modeling | |
650 | 4 | |a residual control charts | |
650 | 4 | |a multistage process monitoring | |
700 | 1 | |a Goh, T.N |4 oth | |
773 | 0 | 8 | |i Enthalten in |t Quality and reliability engineering international |d Chichester [u.a.] : Wiley, 1985 |g 32(2016), 5, Seite 1785-1794 |w (DE-627)129167614 |w (DE-600)50641-2 |w (DE-576)028403312 |x 0748-8017 |7 nnns |
773 | 1 | 8 | |g volume:32 |g year:2016 |g number:5 |g pages:1785-1794 |
856 | 4 | 1 | |u http://dx.doi.org/10.1002/qre.1913 |3 Volltext |
856 | 4 | 2 | |u http://onlinelibrary.wiley.com/doi/10.1002/qre.1913/abstract |
856 | 4 | 2 | |u http://search.proquest.com/docview/1803380603 |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-UMW | ||
912 | |a SSG-OLC-ARC | ||
912 | |a SSG-OLC-TEC | ||
912 | |a SSG-OLC-WIW | ||
912 | |a GBV_ILN_70 | ||
936 | b | k | |a 50.16 |q AVZ |
936 | b | k | |a 85.38 |q AVZ |
951 | |a AR | ||
952 | |d 32 |j 2016 |e 5 |h 1785-1794 |
author_variant |
m z mz |
---|---|
matchkey_str |
article:07488017:2016----::fetomdlcuayneiul |
hierarchy_sort_str |
2016 |
bklnumber |
50.16 85.38 |
publishDate |
2016 |
allfields |
10.1002/qre.1913 doi PQ20160720 (DE-627)OLC1979247501 (DE-599)GBVOLC1979247501 (PRQ)p1193-5ba87fd202171378d392e1cca01f8b61365a39d92eeabd2ff278e2a886647853 (KEY)0136540120160000032000501785effectsofmodelaccuracyonresidualcontrolcharts DE-627 ger DE-627 rakwb eng 650 690 DNB 50.16 bkl 85.38 bkl Zhou, Min verfasserin aut Effects of Model Accuracy on Residual Control Charts 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd. Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. average run length multivariate autogressive modeling residual control charts multistage process monitoring Goh, T.N oth Enthalten in Quality and reliability engineering international Chichester [u.a.] : Wiley, 1985 32(2016), 5, Seite 1785-1794 (DE-627)129167614 (DE-600)50641-2 (DE-576)028403312 0748-8017 nnns volume:32 year:2016 number:5 pages:1785-1794 http://dx.doi.org/10.1002/qre.1913 Volltext http://onlinelibrary.wiley.com/doi/10.1002/qre.1913/abstract http://search.proquest.com/docview/1803380603 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_70 50.16 AVZ 85.38 AVZ AR 32 2016 5 1785-1794 |
spelling |
10.1002/qre.1913 doi PQ20160720 (DE-627)OLC1979247501 (DE-599)GBVOLC1979247501 (PRQ)p1193-5ba87fd202171378d392e1cca01f8b61365a39d92eeabd2ff278e2a886647853 (KEY)0136540120160000032000501785effectsofmodelaccuracyonresidualcontrolcharts DE-627 ger DE-627 rakwb eng 650 690 DNB 50.16 bkl 85.38 bkl Zhou, Min verfasserin aut Effects of Model Accuracy on Residual Control Charts 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd. Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. average run length multivariate autogressive modeling residual control charts multistage process monitoring Goh, T.N oth Enthalten in Quality and reliability engineering international Chichester [u.a.] : Wiley, 1985 32(2016), 5, Seite 1785-1794 (DE-627)129167614 (DE-600)50641-2 (DE-576)028403312 0748-8017 nnns volume:32 year:2016 number:5 pages:1785-1794 http://dx.doi.org/10.1002/qre.1913 Volltext http://onlinelibrary.wiley.com/doi/10.1002/qre.1913/abstract http://search.proquest.com/docview/1803380603 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_70 50.16 AVZ 85.38 AVZ AR 32 2016 5 1785-1794 |
allfields_unstemmed |
10.1002/qre.1913 doi PQ20160720 (DE-627)OLC1979247501 (DE-599)GBVOLC1979247501 (PRQ)p1193-5ba87fd202171378d392e1cca01f8b61365a39d92eeabd2ff278e2a886647853 (KEY)0136540120160000032000501785effectsofmodelaccuracyonresidualcontrolcharts DE-627 ger DE-627 rakwb eng 650 690 DNB 50.16 bkl 85.38 bkl Zhou, Min verfasserin aut Effects of Model Accuracy on Residual Control Charts 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd. Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. average run length multivariate autogressive modeling residual control charts multistage process monitoring Goh, T.N oth Enthalten in Quality and reliability engineering international Chichester [u.a.] : Wiley, 1985 32(2016), 5, Seite 1785-1794 (DE-627)129167614 (DE-600)50641-2 (DE-576)028403312 0748-8017 nnns volume:32 year:2016 number:5 pages:1785-1794 http://dx.doi.org/10.1002/qre.1913 Volltext http://onlinelibrary.wiley.com/doi/10.1002/qre.1913/abstract http://search.proquest.com/docview/1803380603 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_70 50.16 AVZ 85.38 AVZ AR 32 2016 5 1785-1794 |
allfieldsGer |
10.1002/qre.1913 doi PQ20160720 (DE-627)OLC1979247501 (DE-599)GBVOLC1979247501 (PRQ)p1193-5ba87fd202171378d392e1cca01f8b61365a39d92eeabd2ff278e2a886647853 (KEY)0136540120160000032000501785effectsofmodelaccuracyonresidualcontrolcharts DE-627 ger DE-627 rakwb eng 650 690 DNB 50.16 bkl 85.38 bkl Zhou, Min verfasserin aut Effects of Model Accuracy on Residual Control Charts 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd. Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. average run length multivariate autogressive modeling residual control charts multistage process monitoring Goh, T.N oth Enthalten in Quality and reliability engineering international Chichester [u.a.] : Wiley, 1985 32(2016), 5, Seite 1785-1794 (DE-627)129167614 (DE-600)50641-2 (DE-576)028403312 0748-8017 nnns volume:32 year:2016 number:5 pages:1785-1794 http://dx.doi.org/10.1002/qre.1913 Volltext http://onlinelibrary.wiley.com/doi/10.1002/qre.1913/abstract http://search.proquest.com/docview/1803380603 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_70 50.16 AVZ 85.38 AVZ AR 32 2016 5 1785-1794 |
allfieldsSound |
10.1002/qre.1913 doi PQ20160720 (DE-627)OLC1979247501 (DE-599)GBVOLC1979247501 (PRQ)p1193-5ba87fd202171378d392e1cca01f8b61365a39d92eeabd2ff278e2a886647853 (KEY)0136540120160000032000501785effectsofmodelaccuracyonresidualcontrolcharts DE-627 ger DE-627 rakwb eng 650 690 DNB 50.16 bkl 85.38 bkl Zhou, Min verfasserin aut Effects of Model Accuracy on Residual Control Charts 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd. Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd. average run length multivariate autogressive modeling residual control charts multistage process monitoring Goh, T.N oth Enthalten in Quality and reliability engineering international Chichester [u.a.] : Wiley, 1985 32(2016), 5, Seite 1785-1794 (DE-627)129167614 (DE-600)50641-2 (DE-576)028403312 0748-8017 nnns volume:32 year:2016 number:5 pages:1785-1794 http://dx.doi.org/10.1002/qre.1913 Volltext http://onlinelibrary.wiley.com/doi/10.1002/qre.1913/abstract http://search.proquest.com/docview/1803380603 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_70 50.16 AVZ 85.38 AVZ AR 32 2016 5 1785-1794 |
language |
English |
source |
Enthalten in Quality and reliability engineering international 32(2016), 5, Seite 1785-1794 volume:32 year:2016 number:5 pages:1785-1794 |
sourceStr |
Enthalten in Quality and reliability engineering international 32(2016), 5, Seite 1785-1794 volume:32 year:2016 number:5 pages:1785-1794 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
average run length multivariate autogressive modeling residual control charts multistage process monitoring |
dewey-raw |
650 |
isfreeaccess_bool |
false |
container_title |
Quality and reliability engineering international |
authorswithroles_txt_mv |
Zhou, Min @@aut@@ Goh, T.N @@oth@@ |
publishDateDaySort_date |
2016-01-01T00:00:00Z |
hierarchy_top_id |
129167614 |
dewey-sort |
3650 |
id |
OLC1979247501 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1979247501</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220217093006.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160720s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/qre.1913</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160720</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1979247501</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1979247501</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)p1193-5ba87fd202171378d392e1cca01f8b61365a39d92eeabd2ff278e2a886647853</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0136540120160000032000501785effectsofmodelaccuracyonresidualcontrolcharts</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">650</subfield><subfield code="a">690</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.16</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">85.38</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Min</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Effects of Model Accuracy on Residual Control Charts</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">average run length</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multivariate autogressive modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">residual control charts</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multistage process monitoring</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Goh, T.N</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Quality and reliability engineering international</subfield><subfield code="d">Chichester [u.a.] : Wiley, 1985</subfield><subfield code="g">32(2016), 5, Seite 1785-1794</subfield><subfield code="w">(DE-627)129167614</subfield><subfield code="w">(DE-600)50641-2</subfield><subfield code="w">(DE-576)028403312</subfield><subfield code="x">0748-8017</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:5</subfield><subfield code="g">pages:1785-1794</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1002/qre.1913</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://onlinelibrary.wiley.com/doi/10.1002/qre.1913/abstract</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1803380603</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-UMW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-ARC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.16</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">85.38</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">32</subfield><subfield code="j">2016</subfield><subfield code="e">5</subfield><subfield code="h">1785-1794</subfield></datafield></record></collection>
|
author |
Zhou, Min |
spellingShingle |
Zhou, Min ddc 650 bkl 50.16 bkl 85.38 misc average run length misc multivariate autogressive modeling misc residual control charts misc multistage process monitoring Effects of Model Accuracy on Residual Control Charts |
authorStr |
Zhou, Min |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)129167614 |
format |
Article |
dewey-ones |
650 - Management & auxiliary services 690 - Buildings |
delete_txt_mv |
keep |
author_role |
aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
0748-8017 |
topic_title |
650 690 DNB 50.16 bkl 85.38 bkl Effects of Model Accuracy on Residual Control Charts average run length multivariate autogressive modeling residual control charts multistage process monitoring |
topic |
ddc 650 bkl 50.16 bkl 85.38 misc average run length misc multivariate autogressive modeling misc residual control charts misc multistage process monitoring |
topic_unstemmed |
ddc 650 bkl 50.16 bkl 85.38 misc average run length misc multivariate autogressive modeling misc residual control charts misc multistage process monitoring |
topic_browse |
ddc 650 bkl 50.16 bkl 85.38 misc average run length misc multivariate autogressive modeling misc residual control charts misc multistage process monitoring |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
author2_variant |
t g tg |
hierarchy_parent_title |
Quality and reliability engineering international |
hierarchy_parent_id |
129167614 |
dewey-tens |
650 - Management & public relations 690 - Building & construction |
hierarchy_top_title |
Quality and reliability engineering international |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)129167614 (DE-600)50641-2 (DE-576)028403312 |
title |
Effects of Model Accuracy on Residual Control Charts |
ctrlnum |
(DE-627)OLC1979247501 (DE-599)GBVOLC1979247501 (PRQ)p1193-5ba87fd202171378d392e1cca01f8b61365a39d92eeabd2ff278e2a886647853 (KEY)0136540120160000032000501785effectsofmodelaccuracyonresidualcontrolcharts |
title_full |
Effects of Model Accuracy on Residual Control Charts |
author_sort |
Zhou, Min |
journal |
Quality and reliability engineering international |
journalStr |
Quality and reliability engineering international |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
600 - Technology |
recordtype |
marc |
publishDateSort |
2016 |
contenttype_str_mv |
txt |
container_start_page |
1785 |
author_browse |
Zhou, Min |
container_volume |
32 |
class |
650 690 DNB 50.16 bkl 85.38 bkl |
format_se |
Aufsätze |
author-letter |
Zhou, Min |
doi_str_mv |
10.1002/qre.1913 |
dewey-full |
650 690 |
title_sort |
effects of model accuracy on residual control charts |
title_auth |
Effects of Model Accuracy on Residual Control Charts |
abstract |
Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd. |
abstractGer |
Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd. |
abstract_unstemmed |
Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-UMW SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-WIW GBV_ILN_70 |
container_issue |
5 |
title_short |
Effects of Model Accuracy on Residual Control Charts |
url |
http://dx.doi.org/10.1002/qre.1913 http://onlinelibrary.wiley.com/doi/10.1002/qre.1913/abstract http://search.proquest.com/docview/1803380603 |
remote_bool |
false |
author2 |
Goh, T.N |
author2Str |
Goh, T.N |
ppnlink |
129167614 |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
author2_role |
oth |
doi_str |
10.1002/qre.1913 |
up_date |
2024-07-04T00:26:14.674Z |
_version_ |
1803606058258661376 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1979247501</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220217093006.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160720s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1002/qre.1913</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160720</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1979247501</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1979247501</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)p1193-5ba87fd202171378d392e1cca01f8b61365a39d92eeabd2ff278e2a886647853</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0136540120160000032000501785effectsofmodelaccuracyonresidualcontrolcharts</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">650</subfield><subfield code="a">690</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.16</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">85.38</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhou, Min</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Effects of Model Accuracy on Residual Control Charts</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Residual control charts are acknowledged to be effective tools for statistical process control of multistage processes. In these monitoring procedures, the models on the stage‐wise correlation should be first derived before the control charts are implemented. Therefore, the monitoring performance is inevitably affected by the model fitting scheme. Most of the previous works are under the assumption that the derived models represent the process behavior perfectly. Far less is known about the effects of the model inaccuracy on the monitoring performance. To investigate the effects of the underlying models on the monitoring performance, residual control charts based on two different modeling schemes are compared in this paper. The results indicate that the charting performance is correlated with the model fitting schemes. That is, a more accurate model will significantly increase the detection power and decrease the false alarm rate as well. Copyright © 2015 John Wiley & Sons, Ltd.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: Copyright © 2015 John Wiley & Sons, Ltd.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">average run length</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multivariate autogressive modeling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">residual control charts</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multistage process monitoring</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Goh, T.N</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Quality and reliability engineering international</subfield><subfield code="d">Chichester [u.a.] : Wiley, 1985</subfield><subfield code="g">32(2016), 5, Seite 1785-1794</subfield><subfield code="w">(DE-627)129167614</subfield><subfield code="w">(DE-600)50641-2</subfield><subfield code="w">(DE-576)028403312</subfield><subfield code="x">0748-8017</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:5</subfield><subfield code="g">pages:1785-1794</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1002/qre.1913</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://onlinelibrary.wiley.com/doi/10.1002/qre.1913/abstract</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://search.proquest.com/docview/1803380603</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-UMW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-ARC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.16</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">85.38</subfield><subfield code="q">AVZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">32</subfield><subfield code="j">2016</subfield><subfield code="e">5</subfield><subfield code="h">1785-1794</subfield></datafield></record></collection>
|
score |
7.39966 |