Multivariate Quality Control Chart for Autocorrelated Processes
Copyright Taylor & Francis.
| Publié dans: | Journal of applied statistics. - 1991. - 31(2004), 3 vom: 07., Seite 317-327 |
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| Auteur principal: | |
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| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2004
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| Accès à la collection: | Journal of applied statistics |
| Sujets: | Journal Article Multivariate statistical process control autocorrelation |
| Résumé: | Copyright Taylor & Francis. Traditional multivariate statistical process control (SPC) techniques are based on the assumption that the successive observation vectors are independent. In recent years, due to automation of measurement and data collection systems, a process can be sampled at higher rates, which ultimately leads to autocorrelation. Consequently, when the autocorrelation is present in the data, it can have a serious impact on the performance of classical control charts. This paper considers the problem of monitoring the mean vector of a process in which observations can be modelled as a first-order vector autoregressive VAR (1) process. We propose a control chart called Z-chart which is based on the single step finite intersection test (Timm, 1996). An important feature of the proposed method is that it not only detects an out of control status but also helps in identifying variable(s) responsible for the out of control situation. The proposed method is illustrated with the help of suitable illustrations |
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| Description: | Date Revised 18.10.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
| ISSN: | 0266-4763 |
| DOI: | 10.1080/0266476042000184000 |