A new sampling strategy to reduce the effect of autocorrelation on a control chart
On-line monitoring of quality characteristics is essential to limit scrap and rework costs due to bad quality in a manufacturing process. In several manufacturing environments, during production process data can be massively collected with high sampling rates and tight sampling frequencies. As a con...
| Authors: | , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2014 |
| Country: | Brasil |
| Institution: | Universidade Estadual Paulista (UNESP) |
| Repository: | Repositório Institucional da UNESP |
| Language: | English |
| OAI Identifier: | oai:repositorio.unesp.br:11449/227734 |
| Online Access: | http://dx.doi.org/10.1080/02664763.2013.871507 http://hdl.handle.net/11449/227734 |
| Access Level: | Open access |
| Keyword: | AR(1) ARL autocorrelation sampling strategy Shewhart control chart |
| Summary: | On-line monitoring of quality characteristics is essential to limit scrap and rework costs due to bad quality in a manufacturing process. In several manufacturing environments, during production process data can be massively collected with high sampling rates and tight sampling frequencies. As a consequence, natural autocorrelation may arise among consecutive measures within a sample. Autocorrelation significantly inflates the average run length of a control chart and deteriorates its sensitivity to the occurrence of assignable causes. In this paper, we propose a new mixed sampling strategy for the Shewhart chart monitoring the sample mean in a process where temporal autocorrelation between two consecutive observations can be represented by means of a first order autoregressive model AR(1). With this strategy, the sample mean at each inspection time is computed by merging measures of a generic quality characteristic from two consecutive samples taken h hours apart. The statistical properties of a Shewhart control chart implementing the proposed strategy are compared to those implementing a skipping strategy recently proposed in literature. A numerical analysis shows that the mixed sampling outperforms the skipping sampling strategy for high levels of autocorrelation. © 2013 © 2013 Taylor & Francis. |
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