Cumulative sum control charts for monitoring process mean and/or variance

Nowadays quality is an extremely important tool in satisfying customers and winning market shares. When a quality problem occurs, it is crucial to detect it quickly in order to avoid serious economic loss. Control chart is a powerful Statistical Process Control (SPC) method to monitor and diagnose t...

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主要作者: Yang, Mei
其他作者: Wu Zhang
格式: Theses and Dissertations
語言:English
出版: 2012
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在線閱讀:https://hdl.handle.net/10356/48014
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spelling sg-ntu-dr.10356-480142023-03-11T17:57:00Z Cumulative sum control charts for monitoring process mean and/or variance Yang, Mei Wu Zhang School of Mechanical and Aerospace Engineering DRNTU::Engineering::Mechanical engineering Nowadays quality is an extremely important tool in satisfying customers and winning market shares. When a quality problem occurs, it is crucial to detect it quickly in order to avoid serious economic loss. Control chart is a powerful Statistical Process Control (SPC) method to monitor and diagnose the processes. The cumulative sum (CUSUM) control chart which accumulates historical information in the process is effective to detect process changes including mean and variance shifts. This thesis proposes several new CUSUM charts in detecting process shifts in mean and/or variance. An optimization model which uses an overall performance measure, Average Extra Quality Loss (AEQL), as the objective function is adopted to design these charts. The performance of these charts is compared with that of the most effective CUSUM charts that can be found in current literature. Furthermore, the effect of sampling cost and the probability distribution of process shifts on charts design and performance has also been investigated. DOCTOR OF PHILOSOPHY (MAE) 2012-02-10T08:55:32Z 2012-02-10T08:55:32Z 2012 2012 Thesis Yang, M. (2012). Cumulative sum control charts for monitoring process mean and/or variance. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/48014 10.32657/10356/48014 en 189 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Mechanical engineering
spellingShingle DRNTU::Engineering::Mechanical engineering
Yang, Mei
Cumulative sum control charts for monitoring process mean and/or variance
description Nowadays quality is an extremely important tool in satisfying customers and winning market shares. When a quality problem occurs, it is crucial to detect it quickly in order to avoid serious economic loss. Control chart is a powerful Statistical Process Control (SPC) method to monitor and diagnose the processes. The cumulative sum (CUSUM) control chart which accumulates historical information in the process is effective to detect process changes including mean and variance shifts. This thesis proposes several new CUSUM charts in detecting process shifts in mean and/or variance. An optimization model which uses an overall performance measure, Average Extra Quality Loss (AEQL), as the objective function is adopted to design these charts. The performance of these charts is compared with that of the most effective CUSUM charts that can be found in current literature. Furthermore, the effect of sampling cost and the probability distribution of process shifts on charts design and performance has also been investigated.
author2 Wu Zhang
author_facet Wu Zhang
Yang, Mei
format Theses and Dissertations
author Yang, Mei
author_sort Yang, Mei
title Cumulative sum control charts for monitoring process mean and/or variance
title_short Cumulative sum control charts for monitoring process mean and/or variance
title_full Cumulative sum control charts for monitoring process mean and/or variance
title_fullStr Cumulative sum control charts for monitoring process mean and/or variance
title_full_unstemmed Cumulative sum control charts for monitoring process mean and/or variance
title_sort cumulative sum control charts for monitoring process mean and/or variance
publishDate 2012
url https://hdl.handle.net/10356/48014
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