改进了适应性CUSUM控制图,用于在测量误差下监控工业过程
Abdullah Ali H Ahmadini1, Imad Khan2, Shokrya Saleh A Alshqaq1
1Department of Mathematics, College of Science, Jazan University, P.O. Box 114, 45142, Jazan, Kingdom of Saudi Arabia.
Scientific reports
|May 13, 2025
概括
本研究引入了改进的自适应累积和 (IACUSUM) 控制图,以解决统计过程控制 (SPC) 中的测量误差 (ME). 这种新方法提高了工业质量保证的故障检测和工艺监测准确度.
科学领域:
- 工业工程 工业工程 工业工程
- 质量控制 质量控制 质量控制
- 统计过程控制 统计过程控制
背景情况:
- 测量误差 (ME) 严重损害了统计过程控制 (SPC) 方法的准确性和可靠性.
- 由于ME,延迟故障检测和受损的过程监控是工业环境中常见的问题.
研究的目的:
- 提出一个改进的自适应累积和 (IACUSUM) 控制图,旨在减轻ME的不良影响.
- 通过严格的模拟和真实数据分析,对IACUSUM图表与传统方法的性能进行评估.
主要方法:
- 在IACUSUM框架内整合线性共变量模型和多重测量程序.
- 使用平均运行长度 (ARL) 和运行长度标准偏差 (SDRL) 的指标进行性能评估.
- 严格的蒙特卡洛模拟和真实数据应用程序来评估图表的有效性.
主要成果:
- 证实ME显著损害了传统控制图的检测能力.
- 与现有方法相比,拟议的IACUSUM控制图表表现出优越的灵敏度,增强的转移检测和更大的稳定性.
- 使用IACUSUM图表的多重测量方法显著提高了过程监控效率.
结论:
- IACUSUM控制图提供了一个实用且可扩展的解决方案,用于在ME存在时提高SPC性能.
- 该方法为具有普遍测量可变性的工业应用提供了可靠的工具.
- 这项工作为质量保证系统适应性控制图表的未来进展奠定了基础.
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The x̄ chart is a statistical tool for monitoring the means in a process.
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