Statistical Control of Multiple-Stream Processes: A Literature Review

Epprecht, Eugenio K.

doi:10.1007/978-3-319-12355-4_4

Cited by 11 publications

(9 citation statements)

References 26 publications

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“…The NASA process resembles the second definition in the sense that multiple measurements are made on a cross-section of the process; namely, all of the bearings are measured by two sensors. We detect some correlation between the streams, but as Epprecht and Simões (2013) note, this violates the assumption, made by most MSP methods, that none is present. Given these process features, PCA and its extensions are a possible monitoring solution.…”

Section: Introductionmentioning

confidence: 57%

Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data

Ketelaere

Hubert

Schmitt

2015

Journal of Quality Technology

116

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High-dimensional and time-dependent data pose significant challenges to Statistical Process Monitoring. Dynamic Principal Component Analysis, Recursive PCA and Moving Window PCA have been proposed to cope with high-dimensional and time-dependent features. We present a comprehensive review of this literature for the practitioner encountering this topic for the first time. We detail the implementation of the aforementioned methods, and direct the reader towards extensions which may be useful to their specific problem. A real data example is presented to help the reader draw connections between the methods and the behavior they display. Furthermore, we highlight several challenges that remain for research in this area.

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Section: Introductionmentioning

confidence: 57%

Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data

Ketelaere

Hubert

Schmitt

2015

Journal of Quality Technology

116

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“…Multiple stream processes are present in different areas of the industry: filling machines in cosmetics, beverage (food filling machine with multiple heads) or pharmaceutical industry (container filling on a multiple-head machine), production of rubber hoses by extrusion etc. [5,12,13].…”

Section: Multiple Stream Processes and Capability Indicesmentioning

confidence: 99%

Tolerance Limit-based Estimation of the Proportion of Non-conforming Parts in a Multiple Stream Process

Pusztai

Kemény

2022

Period. Polytech. Chem. Eng.

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The conventional way to characterize the proportion of non-conforming parts in a process is to calculate process capability indices and transform them into a ratio. These widely used indices are able to give digestible information about the ratio of non-conforming parts if some assumptions are fulfilled. A correct estimation method should be based on the output distribution of the process, and the uncertainty of the parameter estimates should be considered, as well. In this article, a special case of the output distribution is examined: a mixture of normal distributions is considered. In practice, this output distribution appears if a multiple stream process is investigated. The novelty of this study is to apply the tolerance interval-based estimation method for the proportion of non-conforming parts in a case study of a multiple stream process and to qualify the limitations of the proposed estimation method. A simulation study is performed to investigate the bias, mean square error, and root mean square error of the estimates from the two estimation methods (process performance index-based and tolerance interval-based) for different sample sizes for each stream (N ). It was found that, if it may be assumed that the speed of the streams is equal in the case of the sample sizes investigated (N = 25, 50, 100 per head), the proposed (tolerance interval-based) method overestimates the proportion of non-conforming parts while the conventional (process performance index-based) method underestimates it. The tolerance-limit based estimation method has asymptotically better properties than the process performance index-based estimation method.

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“…Some further works on the monitoring of MSPs are Mortell & Runger (1995), Runger et al (1996), Amin & Li, (2000), Lanning et al (2002), Liu et al (2008), Xiang & Tsung (2008), Meneces et al (2008) & Epprecht et al (2011a. For a comprehensive literature review of this topic, which describes these works with some detail & includes other references, see Epprecht (2015). Mortell & Runger (1995) were pioneers in acknowledging the fact that many (if not most) MSPs have two separate sources of variation and thereby in such processes the value of the quality variable measured in time t in each stream should be represented by the sum of two random variables: a "mean level" common to all streams (and which may be stable or present some dynamics over time) plus an individual component responsible for the difference between the stream value and the common component value.…”

Section: Introductionmentioning

confidence: 99%

An EWMA control chart for the mean of individual streams in multiple stream processes

Simões

Epprecht

2021

Gest. Prod.

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In a multiple stream process (MSP) a product is manufactured in a number of streams in parallel. The traditional tool for monitoring MSPs, the group control chart (GCC), does not take into account that typically the value of the quality variable in each stream is the sum of a component common to all streams and an individual component, of the particular stream. This may render the GCC ineffective in detecting shifts in the mean of individual streams. Based on this two-components model, we propose an exponentially weighted moving average (EWMA) GCC to monitor the means of the individual streams components. We optimize its design (minimizing the ARL for given shifts in the mean of a stream) and compare their ARLs with the ones of other existing charts devised for two-components MSPs. For this comparison, we needed to obtain optimal designs of these previous charts too, which were not available in the literature; this is an additional contribution of our work. The ARLs of the charts were obtained by simulation, with a number of runs sufficiently large to ensure precise results. The results show that the proposed chart outperforms the previous ones, becoming thus recommended for the statistical control of MSPs.

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Statistical Control of Multiple-Stream Processes: A Literature Review

Cited by 11 publications

References 26 publications

Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data

Overview of PCA-Based Statistical Process-Monitoring Methods for Time-Dependent, High-Dimensional Data

Tolerance Limit-based Estimation of the Proportion of Non-conforming Parts in a Multiple Stream Process

An EWMA control chart for the mean of individual streams in multiple stream processes

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