ABSTRACT

Classical monitoring schemes are typically designed under the assumption of known process parameters, perfect measurements and normality. In real-life applications, these assumptions are often violated. Thus, their Phase II performances are negatively affected by both measurement errors and parameter estimation. In this paper, the performance of the homogenously weighted moving average (HWMA) scheme is investigated under the assumption of unknown process parameters with and without measurement errors using the characteristics of the run-length distribution through intensive simulations. The negative effect of measurement errors is reduced using multiple measurements sampling strategy. It is found that the negative effect of the measurement errors is higher as the smoothing parameter increases and the larger the Phase I sample size, the smaller the negative effect of measurement errors. An illustrative example is given to demonstrate the implementation in real-life applications.

摘要

典型的监视方案通常是在已知过程参数，完美测量值和正常性的假设下设计的。在实际应用中，经常会违反这些假设。因此，它们的II期性能受到测量误差和参数估计的负面影响。在本文中，通过密集仿真通过游程长度分布的特性，研究了均质加权移动平均（HWMA）方案的性能，该方案在未知工艺参数的情况下（带有和不带有测量误差）进行了研究。使用多种测量采样策略可以减少测量误差的负面影响。我们发现，随着平滑参数的增加和I相样本数量的增加，测量误差的负面影响更大，测量误差的负面影响越小。给出了一个说明性示例，以演示在实际应用中的实现。