Multivariate statistical process control charts: an overview

Bersimis, Sotiris; Psarakis, Stelios; Panaretos, John

doi:10.1002/qre.829

Cited by 506 publications

(352 citation statements)

References 189 publications

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“…The results were computed based on the correctly classified patterns. The results support the conclusion that the mean shift with larger magnitudes can be identified more quickly with shorter ARL 1 .…”

Section: A Average Run Lengthssupporting

confidence: 86%

“…Numerous multivariate statistical process control (MSPC) schemes have been proposed for monitoring and diagnosing multivariate process mean shift. A review on traditional MSPC charts and multivariate statistical techniques can be found in [1]. The traditional MSPC charts refer to the T 2 , multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA), whereas the multivariate statistical techniques refer to the principal component analysis (PCA) and partial least square (PLS).…”

Section: Introductionmentioning

confidence: 99%

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Statistical features-ANN recognizer for bivariate process mean shift pattern recognition

Masood

Hassan

2010

2010 International Conference on Intelligent and Advanced Systems

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-Artificial neural network (ANN)-based recognizers have been developed for monitoring and diagnosis bivariate process mean shift in multivariate statistical process control (MSPC). They have better average run lengths (ARLs) performance in monitoring process mean shifts and gave an useful diagnosis information compared to the traditional MSPC schemes such as T 2 , multivariate cumulative sum (MCUSUM) and multivariate exponentially weighted moving average (MEWMA). The existing recognizers are raw databased, whereby raw data input representation were applied into ANN. This approach required in a large network size, more computational effort and training time consuming. In this paper, the statistical features input representation was investigated, whereby the raw data were transformed into exponentially weighted moving average, multiplication of mean with standard deviation and multiplication of mean with meansquare value. The statistical features-ANN recognizer resulted in smaller network size, fast training time, better ARLs for monitoring process mean shifts and comparable recognition accuracy for diagnosing the source variable(s) compared to the raw data-ANN recognizer.

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Section: A Average Run Lengthssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Statistical features-ANN recognizer for bivariate process mean shift pattern recognition

Masood

Hassan

2010

2010 International Conference on Intelligent and Advanced Systems

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“…In quality prediction, PCA is used to define the new set of variables by transforming several correlated manufacturing operation variables. PCA is used to develop a prediction model from a historical dataset when product quality data are not available [30]. The product quality is monitored based on the transformed manufacturing operation variables.…”

Section: Related Workmentioning

confidence: 99%

A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing

Arif¹,

Suryana²,

Hussin³

2013

IJCA

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Quality prediction model has been developed in various industries to realize the faultless manufacturing. However, most of quality prediction model is developed in single-stage manufacturing. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing effectively. This study is intended to propose combination of multiple PCA+ID3 algorithm to develop quality prediction model in MMS. This technique is applied to a semiconductor manufacturing dataset using the cascade prediction approach. The result shows that the combination of multiple PCA+ID3 is manage to produce the more accurate prediction model in term of classifying both positive and negative classes. General TermsData Mining, Prediction Model.

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“…For this reason, in the industry, there are many situations in which the simultaneous monitoring or control of two or more related process characteristics is necessary [8] [9]. In the literature, SPM of multiple variables is collectively known as multivariate SPM [8].…”

Section: Introductionmentioning

confidence: 99%

“…Practitioners using SPM are forced to interpret a huge amount of quality indicators related to each prod-uct characteristic and thus, they encounter diculties when monitoring multivariate production processes because monitoring all quality characteristics independently can be very misleading [8].…”

Section: Introductionmentioning

confidence: 99%

Monitoring high complex production processes using process capability indices

de-Felipe

Benedito

2017

Int J Adv Manuf Technol

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The increasing demand and the globalization of the market are leading to increasing levels of quality in production processes, and thus, nowadays multiple product characteristics must be tested because they are considered critical. In this context, decision makers are forced to interpret a huge amount of quality indicators, when monitoring production processes. This fact leads to a misunderstanding as a result of information overload. The aim of this paper is to help practitioners when monitoring the capability of processes with a huge amount of product characteristics. We propose a methodology that reduces the amount of data in capability analysis by structuring hierarchically the multiple quality indicators obtained in the quality tests. The proposed methodology may help practitioners and decision makers of the industry in three aspects of statistical process monitoring: To identify the part of a complex production process that presents capability problems; to detect worsening over the time in multivariate production processes; and to compare similar production processes. Some illustrative examples based on dierent kinds of production processes are discussed in order to illustrate the methodology. A case of study based on a real production process of the automotive industry is analyzed using the proposed methodology. We conclude that the proposed methodology reduces the necessary amount of data in capability analysis; and thus, that it provides an added value of great interest for managers and decision makers.

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Multivariate statistical process control charts: an overview

Cited by 506 publications

References 189 publications

Statistical features-ANN recognizer for bivariate process mean shift pattern recognition

Statistical features-ANN recognizer for bivariate process mean shift pattern recognition

A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing

Monitoring high complex production processes using process capability indices

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