High-dimensional data is becoming increasingly important in modern manufacturing environments, while brings difficulties for process monitoring at the same time, especially for the detection of sparse mean shifts. Several control schemes like VS-MEWMA and LEWMA have been proposed on basis of variable selection techniques recently. However, these schemes with constant smoothing parameters may perform poorly when the actual magnitude of mean shifts is significantly different from the assumed one. In this paper, to solve this problem, we focus on obtaining the optimal smoothing parameter of a specific shift range. It is proposed to minimize the expectation weighted run length (EWRL) by assigning a probability distribution to the shift magnitude. Therefore two improved MEWMA control charts that adaptively obtain the optimal parameters are proposed, leading to adaptive VS-MEWMA (AVS-MEWMA) and adaptive LEWMA (ALEWMA) schemes for succeeding in monitoring sparse mean shifts. The superiorities of the proposed scheme are illustrated by a real data example.

高维数据在现代制造环境中变得越来越重要，同时也给过程监控带来了困难，特别是对于稀疏均值偏移的检测。最近已经提出了几种基于变量选择技术的控制方案，如 VS-MEWMA 和 LEWMA。然而，当均值偏移的实际幅度与假设的明显不同时，这些具有恒定平滑参数的方案可能表现不佳。在本文中，为了解决这个问题，我们专注于获得特定换档范围的最佳平滑参数。建议通过为移位幅度分配概率分布来最小化期望加权运行长度 (EWRL)。因此提出了两种自适应获得最优参数的改进的MEWMA控制图，导致成功监测稀疏均值漂移的自适应 VS-MEWMA (AVS-MEWMA) 和自适应 LEWMA (ALEWMA) 方案。一个真实的数据例子说明了所提出方案的优越性。