Response to the letter by Professors Woodall and Knoth

IF 1.3 4区 工程技术 Q4 ENGINEERING, INDUSTRIAL
Nasir Abbas
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引用次数: 0

Abstract

Dear Professor Edwards: I am thankful to Professors Woodall and Knoth for their comments on my article “On Efficient Change Point Detection using a Step Cumulative Sum Control Chart” and wouldmake an effort to address each of their concerns. Referring to some statements from Page (1961), Duncan (1986) and Montgomery (2013), Professors Woodall and Knoth argued that smaller shifts in the process parameters are not desired to be detected. Based on the said arguments, they concluded that classical cumulative sum (CUSUM) performs better (overall) as compared to the step-CUSUM, if designed for shifts of size 2.5 standard error or larger. In my opinion, shifts of sizes more than 2.5 standard error are quite large and memory-type control charts are not usually designed for such shifts. In example 1.9.1 for monitoring the diameter of 5mm bolts, Hawkins and Olwell (1998, 28) used the design parameter k 1⁄4 0:25 for demonstration of CUSUM chart which is corresponding to optimizing the chart for shift size dTarget 1⁄4 0:5: Further, Hawkins and Olwell (1998, 32) recommended choosing a value of k that targets “a shift large enough to have a meaningful impact on the process operation but small enough not to be obvious to the naked eye.” Writing about the situations where detecting small shifts is important, they further added “If however a shift of one standard deviation is large enough to affect the process, then not being able to detect it reliably could be a problem, and then tuning to d 1⁄4 3 could be a bad choice.” In another well-known book on statistical process control, while recommending the design parameter of CUSUM chart in Table 9.3, Montgomery (2013, 422) gave results at k 1⁄4 0:5 which corresponds to optimizing the chart for dTarget 1⁄4 1: Likewise, on choosing the design parameter of CUSUM chart, Ryan (2011, 266) stated “We have seen that an X-chart is effective in detecting a largemean shift such as a 3or 4-sigma shift. Therefore, there would be no point in setting k 1⁄4 1:5 or k 1⁄4 2:0: Theusual choice is k 1⁄4 0:5, which is the appropriate choice for detecting a 1-sigma shift.” Considering these recommendations about the design parameter of CUSUM chart, it seemsunreasonable to design/compare the performance of twomemory-type control charts for larger shifts of sizesmore than 2.5 standard error. Professors Woodall and Knoth also commented on the usefulness of combined Shewhart-CUSUM chart for situations where the step-CUSUM is performing better than classical CUSUM. I should emphasize that in the original paper Abbas (2023, 12), the performance comparison (in terms of zeroand steadystate average run lengths) of step-CUSUM with the combined Shewhart-CUSUM is already given in Tables 17 and 18. The overall comparison using the extra quadratic loss (EQL) metric showed that the step-CUSUM is slightly better than the combined Shewhart-CUSUM chart. In addition, the design structure and implementation procedure of a combined Shewhart-step-CUSUM is also proposed. This combination works even better than the step-CUSUM in terms of EQL. In their second last paragraph, Professors Woodall and Knoth mentioned structural details and some drawbacks of progressive mean (PM), double progressive mean (DPM) and mixed EWMA-CUSUM charts. As can be observed in Abbas (2023, Section 4), I tend to agree with their assessment of the steady-state performance of PM and DPM charts. Although these control charts offer excellent zero-state ARL performance, the performance deteriorates for delayed shifts. Nevertheless, these structures can be useful for some shortrun processes. Regarding mixed EWMA-CUSUM chart, I believe more research needs to be done on identifying its optimal design parameters for particular values of dTarget: Only then would it be possible to compare the performances fairly and pinpoint areas of superiority. Finally, I appreciate Professors Woodall and Knoth’s input and welcome this positive debate. I want to express my gratitude to the editor of Quality Engineering for extending the invitation to write this letter.
Woodall和Knoth教授对这封信的回应
尊敬的Edwards教授:我感谢Woodall教授和Knoth教授对我的文章《关于使用阶跃累积和控制图的有效变化点检测》的评论,并将努力解决他们的每一个问题。Woodall和Knoth教授在提到Page(1961)、Duncan(1986)和Montgomery(2013)的一些陈述时认为,不希望检测到工艺参数的较小变化。基于上述论点,他们得出结论,如果设计用于2.5标准误差或更大的偏移,则与步骤CUSUM相比,经典累积和(CUSUM)表现更好(总体)。在我看来,大小超过2.5标准误差的偏移是相当大的,并且存储器类型的控制图通常不是为这种偏移而设计的。在示例1.9.1中,为了监测5mm螺栓的直径,Hawkins和Olwell(1998,28)使用设计参数k 1⁄4 0:25来演示CUSUM图表,该图表对应于优化图表的偏移尺寸D目标1⁄4:5:此外,Hawkins和Olwell(1998,32)建议选择一个k值,该值的目标是“一个足够大的偏移,对工艺操作产生有意义的影响,但足够小,肉眼看不到”,他们进一步补充道:“然而,如果一个标准偏差的偏移足够大,足以影响过程,那么无法可靠地检测它可能是一个问题,然后调整到d 1⁄4 3可能是一种糟糕的选择。”在另一本关于统计过程控制的知名书中,在推荐表9.3中CUSUM图表的设计参数时,Montgomery(2013422)在k 1⁄4 0:5给出了结果,这对应于优化dTarget 1⁄41的图表:同样,在选择CUSUM图表的设计参数时,Ryan(2011266)指出:“我们已经看到,X图可以有效地检测较大的平均位移,如3或4西格玛位移。因此,设置k 1⁄4 1:5或k 1⁄4 2:0没有意义:通常的选择是k 1⁄0:5,这是检测1西格玛位移的合适选择。”考虑到这些关于CUSUM图设计参数的建议,对于大于2.5标准误差的较大尺寸偏移,设计/比较两个记忆型控制图的性能似乎是不合理的。Woodall和Knoth教授还评论了组合休哈特CUSUM图表在步骤CUSUM比经典CUSUM表现更好的情况下的有用性。我应该强调的是,在原始论文Abbas(2023,12)中,表17和18中已经给出了阶跃CUSUM与组合休哈特CUSUM的性能比较(根据零和稳态平均行程长度)。使用额外二次损失(EQL)度量的总体比较表明,阶跃CUSUM略好于组合的休哈特CUSUM图。此外,还提出了组合休哈特步骤CUSUM的设计结构和实现过程。就EQL而言,这种组合甚至比步骤CUSUM效果更好。Woodall和Knoth教授在他们的倒数第二段中提到了渐进均值(PM)、双渐进均值(DPM)和混合EWMA-CUSUM图的结构细节和一些缺点。正如在Abbas(2023,第4节)中所观察到的,我倾向于同意他们对PM和DPM图表稳态性能的评估。尽管这些控制图提供了出色的零状态ARL性能,但延迟换档会使性能恶化。然而,这些结构对于一些短期过程是有用的。关于混合EWMA-CUSUM图表,我认为需要做更多的研究来确定其针对特定dTarget值的最佳设计参数:只有这样,才能公平地比较性能并确定优势领域。最后,我感谢Woodall教授和Knoth教授的意见,并欢迎这场积极的辩论。我想对《质量工程》杂志的编辑发出写这封信的邀请表示感谢。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Quality Engineering
Quality Engineering ENGINEERING, INDUSTRIAL-STATISTICS & PROBABILITY
CiteScore
3.90
自引率
10.00%
发文量
52
审稿时长
>12 weeks
期刊介绍: Quality Engineering aims to promote a rich exchange among the quality engineering community by publishing papers that describe new engineering methods ready for immediate industrial application or examples of techniques uniquely employed. You are invited to submit manuscripts and application experiences that explore: Experimental engineering design and analysis Measurement system analysis in engineering Engineering process modelling Product and process optimization in engineering Quality control and process monitoring in engineering Engineering regression Reliability in engineering Response surface methodology in engineering Robust engineering parameter design Six Sigma method enhancement in engineering Statistical engineering Engineering test and evaluation techniques.
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