Evaluating the effect of measurement errors on the performance of the variable sampling intervals Hotelling's T 2 control charts

2018 ◽  
Vol 34 (8) ◽  
pp. 1785-1799 ◽  
Author(s):  
Hamed Sabahno ◽  
Amirhossein Amiri ◽  
Philippe Castagliola
Keyword(s):  
Measurement Errors ◽  
Control Charts ◽  
Variable Sampling ◽  
Sampling Intervals

EWMA control charts with variable sample sizes and variable sampling intervals

2001 ◽  
Vol 33 (6) ◽  
pp. 511-530 ◽  
Author(s):  
MARION R. REYNOLDS ◽  
JESSE C. ARNOLD
Keyword(s):  
Control Charts ◽  
Sample Sizes ◽  
Variable Sampling ◽  
Sampling Intervals

scholarly journals Nonparametric signed-rank control charts with variable sampling intervals

2017 ◽  
Vol 33 (8) ◽  
pp. 2181-2192 ◽  
Author(s):  
M.L.I. Coelho ◽  
M.A. Graham ◽  
S. Chakraborti
Keyword(s):  
Control Charts ◽  
Signed Rank ◽  
Variable Sampling ◽  
Sampling Intervals

scholarly journals Economic Design of EWMA Control Charts with Variable Sampling Intervals for Monitoring the Mean and Standard Deviation under Preventive Maintenance and Taguchi’s Loss Functions

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Li Xue ◽  
Zhen He
Keyword(s):  
Standard Deviation ◽  
Control Chart ◽  
Economic Model ◽  
Preventive Maintenance ◽  
Control Charts ◽  
Loss Functions ◽  
Variable Sampling ◽  
Sampling Intervals ◽  
Optimal Values ◽  
The Mean

The control chart and the maintenance management need to control process quality and reduce out-of-control cost. They are two key tools in the production process; however, they have usually been analyzed separately in the literature. Moreover, the existing studies integrating these two concepts suffer from three significant drawbacks as follows: (1) using control charts with fixed parameters to monitor the process, so that the small and middle shifts are detected slowly; (2) monitoring the mean and standard deviation separately, whereas, in real condition, the mean and standard deviation should be monitored simultaneously; (3) quality loss function is not usually used to design economic model, which leads to a large social quality loss in the monitoring process of control chart. To eliminate these weaknesses, the economic design of the exponential weighted moving average (EWMA) control chart with variable sampling intervals (VSI) for monitoring the mean and standard deviation under preventive maintenance and Taguchi’s loss functions is proposed. The optimal values of the parameters are determined to minimize the loss-per-item in an average cycle process. In addition, a genetic algorithm is used in a numerical example to search for the optimal values of the parameters. According to the sensitivity analysis, the effect of the model parameters on the solution of the economic model is obtained. Finally, the comparison study shows that the VSI EWMA control charts designed by the joint economic model are expected to reduce loss.

scholarly journals Modified One-Sided EWMA Charts without- and with Variable Sampling Intervals for Monitoring a Normal Process

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 159
Author(s):  
Xuelong Hu ◽  
Suying Zhang ◽  
Guan Sun ◽  
Jianlan Zhong ◽  
Shu Wu
Keyword(s):  
Control Charts ◽  
Moving Average ◽  
Real Data ◽  
Simulation Method ◽  
Monte Carlo Simulation Method ◽  
Variable Sampling ◽  
Distributed Process ◽  
Sampling Intervals ◽  
The One ◽  
Weighted Moving Average

Much research has been conducted on two-sided Exponentially Weighted Moving Average (EWMA) control charts, while less work has been devoted to the one-sided EWMA charts. Traditional one-sided EWMA charts involve resetting the EWMA statistic to the target whenever it falls below or above the target, or truncating the observations above or below the target and further applying the EWMA statistic to the truncated samples. In order to further improve the performance of traditional one-sided EWMA mean (X¯) charts, this paper studies the performance of the Modified One-sided EWMA (MOEWMA) X¯ charts to monitor a normally distributed process. The Monte-Carlo simulation method is used to obtain the zero- and steady-state Run Length (RL) properties of the proposed control charts. Through extensive simulations and comparisons with other charts, it is shown that the proposed MOEWMA X¯ charts compare favorably with some existing competing charts. Moreover, by attaching the variable sampling intervals (VSI) feature to the MOEWMA X¯ charts, it is shown that the VSI MOEWMA charts outperform the corresponding charts without the VSI feature. Finally, a real data example from manufacturing process shows the implementation of the proposed one-sided charts.

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