Ahmadini Abdullah Ali H, Khan Imad, Alshqaq Shokrya Saleh A, AlQadi Hadeel, Ghodhbani Refka, Ahmad Bakhtiyar
Department of Mathematics, College of Science, Jazan University, P.O. Box 114, 45142, Jazan, Kingdom of Saudi Arabia.
Abdul Wali Khan University Mardan, Mardan, Pakistan.
Sci Rep. 2025 May 13;15(1):16616. doi: 10.1038/s41598-025-01734-4.
Measurement error (ME) is a critical factor that affects the accuracy and reliability of statistical process control (SPC) methods, often leading to delayed fault detection and compromised process monitoring. This study proposes an improved adaptive cumulative sum (IACUSUM) control chart that effectively mitigates the adverse effects of ME by integrating a linear covariate model and a multiple measurement procedure. The performance of the proposed chart is evaluated using average run length (ARL) and standard deviation of run length (SDRL) through rigorous Monte Carlo simulations and real-data applications. The findings demonstrate that ME significantly impacts the detection capability of control charts, underscoring the need for effective error management strategies. The IACUSUM control chart, when implemented with a multiple measurement approach, exhibits superior sensitivity, enhanced shift detection, and greater robustness compared to conventional methods. The results confirm that the proposed methodology significantly improves process monitoring efficiency, making it a highly reliable tool for industrial applications where measurement variability is prevalent. This study provides a practical and scalable solution for enhancing SPC performance and sets the foundation for further advancements in adaptive control charts for real-world quality assurance systems.
测量误差（ME）是影响统计过程控制（SPC）方法准确性和可靠性的关键因素，常常导致故障检测延迟和过程监控受损。本研究提出了一种改进的自适应累积和（IACUSUM）控制图，通过整合线性协变量模型和多重测量程序，有效减轻了测量误差的不利影响。通过严格的蒙特卡洛模拟和实际数据应用，使用平均运行长度（ARL）和运行长度标准差（SDRL）对所提出控制图的性能进行了评估。研究结果表明，测量误差对控制图的检测能力有显著影响，凸显了有效误差管理策略的必要性。与传统方法相比，采用多重测量方法实施的IACUSUM控制图具有更高的灵敏度、更强的偏移检测能力和更大的稳健性。结果证实，所提出的方法显著提高了过程监控效率，使其成为测量变异性普遍存在的工业应用中高度可靠的工具。本研究为提高SPC性能提供了实用且可扩展的解决方案，并为实际质量保证系统中自适应控制图的进一步发展奠定了基础。
