Multivariate coefficient of variation control charts in phase I of SPC
Abstract Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigate...
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| Autor*in: |
Abbasi, Saddam Akber [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - Springer London, 1985, 99(2018), 5-8 vom: 29. Aug., Seite 1903-1916 |
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volume:99 ; year:2018 ; number:5-8 ; day:29 ; month:08 ; pages:1903-1916 |
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| DOI / URN: |
10.1007/s00170-018-2535-3 |
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OLC2026128324 |
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| 520 | |a Abstract Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigated the performance of the multivariate CV charts in phase I. The phase I procedures are more important and involve the estimation of the charts’ limits from a historical or reference dataset that represents the in-control state of the process. In real life, contaminations are mostly present in the historical samples; hence, the phase I procedures are mostly adopted to get rid of these contaminated samples. In this study, we investigate the performance of a variety of multivariate CV charts in phase I considering both diffuse symmetric and localized CV disturbance scenarios, using probability to signal as a performance measure. A real-life application, concerning carbon fiber tubing, is also provided to show the implementation of the proposed charts in phase I. The findings of this study will be useful for practitioners in their selection of an efficient phase I control chart for monitoring multivariate CV. | ||
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10.1007/s00170-018-2535-3 doi (DE-627)OLC2026128324 (DE-He213)s00170-018-2535-3-p DE-627 ger DE-627 rakwb eng 670 VZ Abbasi, Saddam Akber verfasserin aut Multivariate coefficient of variation control charts in phase I of SPC 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigated the performance of the multivariate CV charts in phase I. The phase I procedures are more important and involve the estimation of the charts’ limits from a historical or reference dataset that represents the in-control state of the process. In real life, contaminations are mostly present in the historical samples; hence, the phase I procedures are mostly adopted to get rid of these contaminated samples. In this study, we investigate the performance of a variety of multivariate CV charts in phase I considering both diffuse symmetric and localized CV disturbance scenarios, using probability to signal as a performance measure. A real-life application, concerning carbon fiber tubing, is also provided to show the implementation of the proposed charts in phase I. The findings of this study will be useful for practitioners in their selection of an efficient phase I control chart for monitoring multivariate CV. Coefficient of variation Multivariate control chart Phase I Probability to signal Statistical process control Adegoke, Nurudeen A. (orcid)0000-0001-7592-5460 aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 99(2018), 5-8 vom: 29. Aug., Seite 1903-1916 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:99 year:2018 number:5-8 day:29 month:08 pages:1903-1916 https://doi.org/10.1007/s00170-018-2535-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 99 2018 5-8 29 08 1903-1916 |
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10.1007/s00170-018-2535-3 doi (DE-627)OLC2026128324 (DE-He213)s00170-018-2535-3-p DE-627 ger DE-627 rakwb eng 670 VZ Abbasi, Saddam Akber verfasserin aut Multivariate coefficient of variation control charts in phase I of SPC 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigated the performance of the multivariate CV charts in phase I. The phase I procedures are more important and involve the estimation of the charts’ limits from a historical or reference dataset that represents the in-control state of the process. In real life, contaminations are mostly present in the historical samples; hence, the phase I procedures are mostly adopted to get rid of these contaminated samples. In this study, we investigate the performance of a variety of multivariate CV charts in phase I considering both diffuse symmetric and localized CV disturbance scenarios, using probability to signal as a performance measure. A real-life application, concerning carbon fiber tubing, is also provided to show the implementation of the proposed charts in phase I. The findings of this study will be useful for practitioners in their selection of an efficient phase I control chart for monitoring multivariate CV. Coefficient of variation Multivariate control chart Phase I Probability to signal Statistical process control Adegoke, Nurudeen A. (orcid)0000-0001-7592-5460 aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 99(2018), 5-8 vom: 29. Aug., Seite 1903-1916 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:99 year:2018 number:5-8 day:29 month:08 pages:1903-1916 https://doi.org/10.1007/s00170-018-2535-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 99 2018 5-8 29 08 1903-1916 |
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10.1007/s00170-018-2535-3 doi (DE-627)OLC2026128324 (DE-He213)s00170-018-2535-3-p DE-627 ger DE-627 rakwb eng 670 VZ Abbasi, Saddam Akber verfasserin aut Multivariate coefficient of variation control charts in phase I of SPC 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigated the performance of the multivariate CV charts in phase I. The phase I procedures are more important and involve the estimation of the charts’ limits from a historical or reference dataset that represents the in-control state of the process. In real life, contaminations are mostly present in the historical samples; hence, the phase I procedures are mostly adopted to get rid of these contaminated samples. In this study, we investigate the performance of a variety of multivariate CV charts in phase I considering both diffuse symmetric and localized CV disturbance scenarios, using probability to signal as a performance measure. A real-life application, concerning carbon fiber tubing, is also provided to show the implementation of the proposed charts in phase I. The findings of this study will be useful for practitioners in their selection of an efficient phase I control chart for monitoring multivariate CV. Coefficient of variation Multivariate control chart Phase I Probability to signal Statistical process control Adegoke, Nurudeen A. (orcid)0000-0001-7592-5460 aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 99(2018), 5-8 vom: 29. Aug., Seite 1903-1916 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:99 year:2018 number:5-8 day:29 month:08 pages:1903-1916 https://doi.org/10.1007/s00170-018-2535-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 99 2018 5-8 29 08 1903-1916 |
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10.1007/s00170-018-2535-3 doi (DE-627)OLC2026128324 (DE-He213)s00170-018-2535-3-p DE-627 ger DE-627 rakwb eng 670 VZ Abbasi, Saddam Akber verfasserin aut Multivariate coefficient of variation control charts in phase I of SPC 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigated the performance of the multivariate CV charts in phase I. The phase I procedures are more important and involve the estimation of the charts’ limits from a historical or reference dataset that represents the in-control state of the process. In real life, contaminations are mostly present in the historical samples; hence, the phase I procedures are mostly adopted to get rid of these contaminated samples. In this study, we investigate the performance of a variety of multivariate CV charts in phase I considering both diffuse symmetric and localized CV disturbance scenarios, using probability to signal as a performance measure. A real-life application, concerning carbon fiber tubing, is also provided to show the implementation of the proposed charts in phase I. The findings of this study will be useful for practitioners in their selection of an efficient phase I control chart for monitoring multivariate CV. Coefficient of variation Multivariate control chart Phase I Probability to signal Statistical process control Adegoke, Nurudeen A. (orcid)0000-0001-7592-5460 aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 99(2018), 5-8 vom: 29. Aug., Seite 1903-1916 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:99 year:2018 number:5-8 day:29 month:08 pages:1903-1916 https://doi.org/10.1007/s00170-018-2535-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 99 2018 5-8 29 08 1903-1916 |
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10.1007/s00170-018-2535-3 doi (DE-627)OLC2026128324 (DE-He213)s00170-018-2535-3-p DE-627 ger DE-627 rakwb eng 670 VZ Abbasi, Saddam Akber verfasserin aut Multivariate coefficient of variation control charts in phase I of SPC 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2018 Abstract Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigated the performance of the multivariate CV charts in phase I. The phase I procedures are more important and involve the estimation of the charts’ limits from a historical or reference dataset that represents the in-control state of the process. In real life, contaminations are mostly present in the historical samples; hence, the phase I procedures are mostly adopted to get rid of these contaminated samples. In this study, we investigate the performance of a variety of multivariate CV charts in phase I considering both diffuse symmetric and localized CV disturbance scenarios, using probability to signal as a performance measure. A real-life application, concerning carbon fiber tubing, is also provided to show the implementation of the proposed charts in phase I. The findings of this study will be useful for practitioners in their selection of an efficient phase I control chart for monitoring multivariate CV. Coefficient of variation Multivariate control chart Phase I Probability to signal Statistical process control Adegoke, Nurudeen A. (orcid)0000-0001-7592-5460 aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 99(2018), 5-8 vom: 29. Aug., Seite 1903-1916 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:99 year:2018 number:5-8 day:29 month:08 pages:1903-1916 https://doi.org/10.1007/s00170-018-2535-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 99 2018 5-8 29 08 1903-1916 |
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Abstract Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigated the performance of the multivariate CV charts in phase I. The phase I procedures are more important and involve the estimation of the charts’ limits from a historical or reference dataset that represents the in-control state of the process. In real life, contaminations are mostly present in the historical samples; hence, the phase I procedures are mostly adopted to get rid of these contaminated samples. In this study, we investigate the performance of a variety of multivariate CV charts in phase I considering both diffuse symmetric and localized CV disturbance scenarios, using probability to signal as a performance measure. A real-life application, concerning carbon fiber tubing, is also provided to show the implementation of the proposed charts in phase I. The findings of this study will be useful for practitioners in their selection of an efficient phase I control chart for monitoring multivariate CV. © Springer-Verlag London Ltd., part of Springer Nature 2018 |
| abstractGer |
Abstract Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigated the performance of the multivariate CV charts in phase I. The phase I procedures are more important and involve the estimation of the charts’ limits from a historical or reference dataset that represents the in-control state of the process. In real life, contaminations are mostly present in the historical samples; hence, the phase I procedures are mostly adopted to get rid of these contaminated samples. In this study, we investigate the performance of a variety of multivariate CV charts in phase I considering both diffuse symmetric and localized CV disturbance scenarios, using probability to signal as a performance measure. A real-life application, concerning carbon fiber tubing, is also provided to show the implementation of the proposed charts in phase I. The findings of this study will be useful for practitioners in their selection of an efficient phase I control chart for monitoring multivariate CV. © Springer-Verlag London Ltd., part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Multivariate control charts are mostly available for monitoring the process mean vector or the covariance matrix. Recently, work has been done on monitoring the multivariate coefficient of variation (CV) in phase II of the statistical process control (SPC). However, no study has investigated the performance of the multivariate CV charts in phase I. The phase I procedures are more important and involve the estimation of the charts’ limits from a historical or reference dataset that represents the in-control state of the process. In real life, contaminations are mostly present in the historical samples; hence, the phase I procedures are mostly adopted to get rid of these contaminated samples. In this study, we investigate the performance of a variety of multivariate CV charts in phase I considering both diffuse symmetric and localized CV disturbance scenarios, using probability to signal as a performance measure. A real-life application, concerning carbon fiber tubing, is also provided to show the implementation of the proposed charts in phase I. The findings of this study will be useful for practitioners in their selection of an efficient phase I control chart for monitoring multivariate CV. © Springer-Verlag London Ltd., part of Springer Nature 2018 |
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Multivariate coefficient of variation control charts in phase I of SPC |
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