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Title: | Optimal Monitoring Methods for Univariate and Multivariate EWMA Control Charts |

Authors: | Huh, Ick |

Advisor: | Viveros, Roman Balakrishnan, Narayanaswamy |

Department: | Mathematics and Statistics |

Keywords: | Differential smoothing;ARL;Survival Gumbel copula |

Publication Date: | Nov-2014 |

Abstract: | Due to the rapid development of technology, quality control charts have attracted more attention from manufacturing industries in order to monitor quality characteristics of interest more effectively. Among many control charts, my research work has focused on the multivariate exponentially weighted moving average (MEWMA) and the univariate exponentially weighted moving average (EWMA) control charts by using the Markov chain method. The performance of the chart is measured by the optimal average run length (ARL). My Ph.D. thesis is composed of the following three contributions. My first research work is about differential smoothing. The MEWMA control chart proposed by Lowry et al. (1992) has become one of the most widely used charts to monitor multivariate processes. Its simplicity, combined with its high sensitivity to small and moderate process mean jumps, is at the core of its appeal. Lowry et al. (1992) advocated equal smoothing of each quality variable unless there is an a priori reason to weigh quality characteristics differently. However, one may have situations where differential smoothing may be justified. For instance: (a) departures in process mean may be different across quality variables, (b) some variables may evolve over time at a much different pace than other variables, and (c) the level of correlation between variables could vary substantially. For these reasons, I focus on and assess the performance of the differentially smoothed MEWMA chart. The case of two quality variables (BEWMA) is discussed in detail. A bivariate Markov chain method that uses conditional distributions is developed for average run length (ARL) calculations. The proposed chart is shown to perform at least as well as Lowry et al. (1992)'s chart, and noticeably better in most other mean jump directions. Comparisons with the recently introduced double-smoothed BEWMA chart and the univariate charts for the independent case show that the proposed differentially smoothed BEWMA chart has superior performance. My second research work is about monitoring skewed multivariate processes. Recently, Xie et al. (2011) studied monitoring bivariate exponential quality measurements using the standard MEWMA chart originally developed to monitor multivariate normal quality data. The focus of my work is on situations where, marginally, the quality measurements may follow not only exponential distributions but also other skewed distributions such as Gamma or Weibull, in any combination. The joint distribution is specified using the Gumbel copula function thus allowing for varying degrees of correlation among the quality measurements. In addition to the standard MEWMA chart, charts based on the largest or smallest of the measurements and on the joint cumulative distribution function or the joint survivor function, are studied in detail. The focus is on the case of two quality measurements, i.e., on skewed bivariate processes. The proposed charts avoid an undesirable feature encountered by Xie et al. (2011) for the standard MEWMA chart where in some cases the off-target average run length turns out to be larger than the on-target one. Using the optimal average run length, our extensive numerical results show that the proposed methods provide an overall good detection performance in most directions. Simulations were performed to obtain the optimal ARL results but the Markov chain method using the empirical CDF of the statistics involved verified the accuracy of the ARL results. In addition, an examination of the effect of correlation on chart performance was undertaken numerically. The methods are easily extendable to more than two variables. Final study is about a new ARL criterion for univariate processes studied in detail in this thesis. The traditional ARL is calculated assuming a given fixed process mean jump and a given time point where the jump occurs, usually taken to be from the very beginning in most chart performance studies. However, Ryu et al. (2010) demonstrated that the assumption of a fixed mean shift might lead to poor performance of control charts when the actual size of the mean shift is significantly different and therefore suggested a new ARL-based performance measure, called expected weighted run length (EWRL), by assuming that the size of the mean shift is not specified but rather it follows a probability distribution. The EWRL becomes the expected value of the weighted ordinary ARL with respect to this distribution. My methods generalize this criterion by allowing the time at which the mean shift occurs to also vary according to a probability distribution. This leads to a joint distribution for the size of the mean shift and the time the shift takes place, then the EWRL is calculated as the weighted expected value with respect to this joint distribution. The benefit of the generalized EWRL is that one can assess the performance of control charts more realistically when the process starts on-target and then the mean shift occurs at some later random time. Moreover, I also propose the effective EWRL, which measures the number of additional process runs that on average are needed to detect a jump in the mean after it happens. I evaluate several well-known univariate control charts based on their EWRL and effective EWRL performance. The numerical results show that the choice of control chart depends on the additional information on the transition point of the mean shift. The methods can readily be extended to other control charts, including multivariate charts. |

URI: | http://hdl.handle.net/11375/15959 |

Appears in Collections: | Open Access Dissertations and Theses |

Files in This Item:

File | Description | Size | Format | |
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Ick_Huh_PhD_Thesis.pdf | Main article | 5.06 MB | Adobe PDF | View/Open |

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