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首页> 外文期刊>International journal of productivity and quality management >Monitoring of process mean and variance simultaneously by joint X and R chart for serial correlation
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Monitoring of process mean and variance simultaneously by joint X and R chart for serial correlation

机译：通过联合X和R图同时监视过程均值和方差以进行序列相关

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It is assumed that the presence of the assignable cause may shift either process mean or standard deviation from their target value. But in the design of joint X and R charts, it is assumed that, both mean and standard deviation of the process change simultaneously. So, for simultaneous monitoring of the process mean and standard deviation, a modified joint X and R chart is suggested in this paper, which can be more useful at the shop floor level. The design of modified joint X and R chart is based upon the sum of chi-squares theory. The performance of joint chart is measured in terms of average run lengths (ARLs) and compared with the joint Shewhart chart and residuals charts, suggested by Karaoglan and Bayhan (2011) for sample size of ten. It is observed that the joint modified X and R chart outperforms all the residual charts at the highest level of correlation, i.e., Φ = 0.95. The simplicity and robustness in the design of the modified joint X and R chart makes it suitable for the industries where the data are highly correlated.
机译：假定可分配原因的存在可能会使过程平均值或标准偏差偏离其目标值。但是在设计联合X和R图时，假定过程的均值和标准差都同时变化。因此，为了同时监视过程均值和标准偏差，本文建议使用修改后的关节X和R图，这在车间级别可能更有用。修改后的关节X和R图的设计基于卡方理论的总和。联合图表的性能以平均游程长度（ARL）衡量，并与Karaoglan和Bayhan（2011）建议的十个样本规模的联合Shewhart图表和残差图表进行比较。可以看出，在最高相关度（即Φ= 0.95）下，联合修改的X和R图优于所有残差图。修改后的X和R联合图表的设计简洁性和鲁棒性使其适用于数据高度相关的行业。

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