In practice, measurement errors exist and ignoring their presence may lead to erroneous conclusions in the actual performance of control charts. The implementation of the existing multivariate coefficient of variation (MCV) charts ignores the presence of measurement errors. To address this concern, the performances of the upward Shewhart‐MCV and exponentially weighted moving average MCV charts for detecting increasing MCV shifts, using a linear covariate error model, are investigated. Explicit mathematical expressions are derived to compute the limits and average run lengths of the charts in the presence of measurement errors. Finally, an illustrative example using a real‐life dataset is presented to demonstrate the charts’ implementation.

中文翻译：

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使用线性协变量误差模型，在存在测量误差的情况下，使用向上的Shewhart和EWMA图监视多元变异系数

在实践中，存在测量错误，而忽略它们的存在可能会导致控制图的实际性能得出错误的结论。现有的多元变异系数（MCV）图表的实现忽略了测量误差的存在。为了解决这个问题，研究了使用线性协变量误差模型对向上Shewhart-MCV和指数加权移动平均MCV图表的性能进行检测，以检测MCV偏移增加的情况。导出明确的数学表达式，以在存在测量误差的情况下计算图表的极限和平均游程长度。最后，给出了一个使用实际数据集的说明性示例，以演示图表的实现。