Publications-Periodical Articles
Article View/Open
Publication Export
-
題名 A Phase II Depth-Based Variable Dimension EWMA Control Chart for Monitoring Process Mean 作者 楊素芬
Yang, Su-Fen
Lin, Yi-Chi
Yeh, Arthur B.貢獻者 統計系 關鍵詞 data depth;exponentially weighted moving average;Phase II monitoring;variable dimension 日期 2021-10 上傳時間 2022-04-12 摘要 Statistical process control consists of tools and techniques that are useful for improving a process or ensuring that a process is in a stable and satisfactory state. In many modern industrial applications, it is critically important to simultaneously monitor two or more correlated process quality variables, thus necessitating the development of multivariate statistical process control (MSPC) as an important area of research for the new century. Nevertheless, the existing MSPC research is mostly based on the assumption that the process data follow a multinormal distribution or a known distribution. However, it is well recognized that in many applications the underlying process distribution is unknown. In practice, among a set of correlated variables to be monitored, there is oftentimes a subset of variables that are easy and/or inexpensive to measure, whereas the remaining variables are difficult and/or expensive to measure but contain information that may help more quickly detect a shift in the process mean. We are motivated to develop a Phase II control chart to monitor variable dimension (VD) mean vector for unknown multivariate processes. The proposed chart is based on the exponentially weighted moving average (EWMA) of a depth-based statistic. The proposed chart is shown to lead to faster detection of mean shifts than the existing VDT2 and VD EWMAT2 charts studied in Aparisi et al. and Epprecht et al., respectively. 關聯 Quality and Reliability International, Vol.37, No.6, pp.2384-2398 資料類型 article DOI https://doi.org/10.1002/qre.2863 dc.contributor 統計系 dc.creator (作者) 楊素芬 dc.creator (作者) Yang, Su-Fen dc.creator (作者) Lin, Yi-Chi dc.creator (作者) Yeh, Arthur B. dc.date (日期) 2021-10 dc.date.accessioned 2022-04-12 - dc.date.available 2022-04-12 - dc.date.issued (上傳時間) 2022-04-12 - dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139853 - dc.description.abstract (摘要) Statistical process control consists of tools and techniques that are useful for improving a process or ensuring that a process is in a stable and satisfactory state. In many modern industrial applications, it is critically important to simultaneously monitor two or more correlated process quality variables, thus necessitating the development of multivariate statistical process control (MSPC) as an important area of research for the new century. Nevertheless, the existing MSPC research is mostly based on the assumption that the process data follow a multinormal distribution or a known distribution. However, it is well recognized that in many applications the underlying process distribution is unknown. In practice, among a set of correlated variables to be monitored, there is oftentimes a subset of variables that are easy and/or inexpensive to measure, whereas the remaining variables are difficult and/or expensive to measure but contain information that may help more quickly detect a shift in the process mean. We are motivated to develop a Phase II control chart to monitor variable dimension (VD) mean vector for unknown multivariate processes. The proposed chart is based on the exponentially weighted moving average (EWMA) of a depth-based statistic. The proposed chart is shown to lead to faster detection of mean shifts than the existing VDT2 and VD EWMAT2 charts studied in Aparisi et al. and Epprecht et al., respectively. dc.format.extent 448101 bytes - dc.format.mimetype application/pdf - dc.relation (關聯) Quality and Reliability International, Vol.37, No.6, pp.2384-2398 dc.subject (關鍵詞) data depth;exponentially weighted moving average;Phase II monitoring;variable dimension dc.title (題名) A Phase II Depth-Based Variable Dimension EWMA Control Chart for Monitoring Process Mean dc.type (資料類型) article dc.identifier.doi (DOI) 10.1002/qre.2863 dc.doi.uri (DOI) https://doi.org/10.1002/qre.2863
