Using Control Charts to Monitor Process and Product Quality Profiles

Woodall, William H.; Spitzner, Dan J.; Montgomery, Douglas C.; Gupta, Shilpa

doi:10.1080/00224065.2004.11980276

Cited by 445 publications

(296 citation statements)

References 47 publications

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“…The pairs of observations (2,1), (4,2), (6,3), and (8,2) are considered as the values for explanatory variables x 1 and x 2 . The vector of error terms (" 1 ; " 2 ) follows a bivariate normal random variable with mean vector zero and known covariance matrix:…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In di erent applications, simple linear regression, multiple linear or polynomial regression, or even more complicated models such as nonlinear regressions are used to model the quality of processes. The applications of pro le monitoring in the literature have been introduced by several authors such as Kang Woodall et al [3], Wang and Tsung [4], Woodall [5], Zou et al [6], and Amiri et al [7]. A pro le monitoring problem consists of two phases, including Phase I and Phase II.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Maximum Multivariate Exponentially Weighted Moving Average and Maximum Multivariate Cumulative Sum Control Charts for Simultaneous Monitoring of Mean and Variability of Multivariate Multiple Linear Regression Profiles

Ghashghaei

Amiri

2017

Scientia Iranica

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Abstract. In some applications, quality of product or performance of a process is described by some functional relationships among some variables known as multivariate linear pro le in the literature. In this paper, we propose Max-MEWMA and Max-MCUSUM control charts for simultaneous monitoring of mean vector and covariance matrix in multivariate multiple linear regression pro les in Phase II. The proposed control charts also have the ability to diagnose whether the location or variation of the process is responsible for out-of-control signal. The performance of the proposed control charts is compared with that of the existing method through Monte-Carlo simulations. Finally, the applicability of the proposed control charts is illustrated using a real case of calibration application in the automotive industry.

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Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maximum Multivariate Exponentially Weighted Moving Average and Maximum Multivariate Cumulative Sum Control Charts for Simultaneous Monitoring of Mean and Variability of Multivariate Multiple Linear Regression Profiles

Ghashghaei

Amiri

2017

Scientia Iranica

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“…Compared with a multivariate dataset, profiles contain even more data point and have to be modeled as an extremely high dimensional problem. Both model-based and model-free methods have been proposed for profile monitoring (see Woodall et al (2004) for an extensive survey).…”

Section: Dealing With High Dimensionalitiesmentioning

confidence: 99%

Adaptive Charting Techniques: Literature Review and Extensions

Tsung

Wang

2010

Frontiers in Statistical Quality Control 9

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Summary. The continuous development of SPC is driven by challenges arising from practical applications across diverse industries. Among others, adaptive charts are becoming more and more popular due to their capability in tackling these challenges by learning unknown shifts and tracking time-varying patterns. This chapter reviews recent development of adaptive charts and classifies them into two categories: those with variable sampling parameters and those with variable design parameters. This review focuses on the latter group and compares their charting performance. As an extension to conventional multivariate charts, this work proposes a double-sided directionally variant chart. The proposed chart is capable of detecting shifts having the same or opposite directions as the reference vector and is more robust to processes with unpredictable shift directions.

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“…We are concerned with ongoing monitoring to detect assignable causes in the process in phase II controlling. Useful recognitions of phase I and phase II applications have been studied already, for example, by Kang and Albin (2000), Woodall (2000), Hawkins et al (2003), Woodall et al (2004), Montgomery (2005), and Jensen et al (2006).…”

Section: Introductionmentioning

confidence: 99%

“…Shewhart (1931) proposed a scheme for detecting out-of-control signals and shifts in the mean from its target value 0 μ . Ott (1975), Rocke (1989), Ryan (1989), Chen (1997), Quesenberry (1997), Smith (1998), Maravelakis, et al, (2002), Woodall et al (2004), Montgomery (2005), and several other authors modified and extended the Shewhart control charts. The Shewhart procedure usually is based on at least 20 to 25 sample group sizes ( k ) and at least 4 to 6 sample subgroup sizes ( n ).…”

Section: Introductionmentioning

confidence: 99%