A simple test to determine the contributors of fraction nonconforming shifts in a multivariate binomial process

IF 1.3 4区工程技术 Q4 ENGINEERING, INDUSTRIAL

Quality Engineering Pub Date : 2022-09-26 DOI:10.1080/08982112.2022.2124876

C. Hou

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引用次数: 0

Abstract

Abstract The fraction nonconforming, which follows a binomial distribution, is one of the most critical quality characteristics of attribute processes. In addition, the multivariate binomial process plays an important role in industries due to the enormous diversity of quality characteristics. A multivariate binomial process is deemed out of control when it triggers a signal in a multivariate statistical process control chart. However, it is difficult to determine which quality characteristic triggers the nonconforming shift. In contrast to most current studies that identify the contributors of shifts in multivariate normal processes, this study discusses the contributors of fraction nonconforming shifts in multivariate binomial processes. First, a test that can be applied to detect outliers in a multivariate binomial distribution is proposed. In addition, a stepwise test method that can be used to determine the contributors of fraction nonconforming shifts in a multivariate binomial process is then developed. Numerical results indicate that the method proposed is effective in determining the contributors of fraction nonconforming shifts for a multivariate binomial process.

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一个简单的测试，以确定在多元二项式过程中的分数不符合移位的贡献者

分数不一致性是属性过程最重要的质量特征之一，它服从二项分布。此外，由于质量特征的巨大多样性，多元二项过程在工业中起着重要作用。在多元统计过程控制图中，当一个多元二项式过程触发一个信号时，就被认为是失控的。然而，很难确定是哪个质量特性引发了不合格转移。与目前大多数研究确定多元正态过程中位移的贡献者不同，本研究讨论了多元二项式过程中分数不符合位移的贡献者。首先，提出了一种检测多元二项分布异常值的检验方法。此外，还提出了一种逐步检验方法，可用于确定多元二项式过程中分数不符合位移的贡献因素。数值结果表明，该方法能有效地确定多元二项式过程分数不一致位移的贡献因子。

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 ENGINEERING, INDUSTRIAL-STATISTICS & PROBABILITY

CiteScore

3.90

自引率

10.00%

发文量

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.