Scholarlink

<p>Statistical process control (SPC) methods are essential for maintaining product quality, with control charts playing a key role in monitoring process stability. This study proposes an efficient adaptive EWMA (AEWMA) charting scheme to detect changes in the proportion of nonconforming products under both zero and steady states. The proposed chart dynamically estimates mean shifts using EWMA statistics and adjusts the smoothing parameter based on shift magnitude. Monte Carlo simulations evaluate their run length (RL) performance, including average RL, standard deviation, and percentiles. Results indicate that the AEWMA chart outperforms conventional methods in detecting persistent process shifts. The study also examines the impact of estimating the nonconforming proportion. Applications using real-world and simulated data validate their effectiveness.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Dynamic smoothing in EWMA charts for improved fraction nonconforming surveillance

  • 期刊论文

  • 发表日期：

    2025年6月30日

查看全文
  • Zameer Abbas,
  • Tahir Abbas,
  • Hafiz Zafar Nazir,
  • Noureen Akhtar

摘要

Statistical process control (SPC) methods are essential for maintaining product quality, with control charts playing a key role in monitoring process stability. This study proposes an efficient adaptive EWMA (AEWMA) charting scheme to detect changes in the proportion of nonconforming products under both zero and steady states. The proposed chart dynamically estimates mean shifts using EWMA statistics and adjusts the smoothing parameter based on shift magnitude. Monte Carlo simulations evaluate their run length (RL) performance, including average RL, standard deviation, and percentiles. Results indicate that the AEWMA chart outperforms conventional methods in detecting persistent process shifts. The study also examines the impact of estimating the nonconforming proportion. Applications using real-world and simulated data validate their effectiveness.