Shewhart-type control charts for variation in phase I data analysis

Human, Schalk William; Chakraborti, S.; Smit, C. F.

doi:10.1016/j.csda.2009.09.026

Cited by 26 publications

(35 citation statements)

References 11 publications

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“…It is generally accepted in the literature that a control chart is implemented in two phases: Phase‐I (so called retrospective phase), to establish and define the stable state of the process and to estimate any unknown parameters; and Phase‐II (prospective phase), to monitor the process (Chakraborti et al ., Human et al ., Zwetsloot et al …”

Section: Introductionmentioning

confidence: 99%

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The Phase I Dispersion Charts for Bivariate Process Monitoring

Saghir

Khan

Chakraborti

2015

Qual. Reliab. Engng. Int.

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Multivariate control charts are usually implemented in statistical process control to monitor several correlated quality characteristics. Process dispersion charts are used to determine the stability of process variation (which is typically done before monitoring the process location/mean). A Phase-I study is generally used when population parameters are unknown. This article develops Phase-I |S| and |G| control charts, to monitor the dispersion of a bivariate normal process. The charting constants are determined to achieve the required nominal false alarm probability (FAP 0 ). The performance of the proposed charts is evaluated in terms of (i) the attained false rate and (ii) the probability of signaling for out-of-control situations. The analysis shows that the proposed Phase-I bivariate charts correctly control the FAP (the false alarm probability) and detect a shift occurring in the bivariate dispersion matrix with adequate probability. An example is given to explain the practical implementation of these charts.

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Section: Introductionmentioning

confidence: 99%

“…). Other recent studies on the Phase‐I control charts include Chakraborti et al ., Human et al ., Saghir, Jones‐Farmer et al ., Kumar and Chakraborti, Ning et al ., Zwetsloot et al ., etc.…”

Section: Introductionmentioning

confidence: 99%

The Phase I Dispersion Charts for Bivariate Process Monitoring

Saghir

Khan

Chakraborti

2015

Qual. Reliab. Engng. Int.

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“…Nowadays, the use of control charts has been broadened to many fields, including economics (Fris en (2009)), medicine (Biau et al (2011)), informatics (Park (2005)), and others. Shewhart control charts are very popular in SPC and can be utilized to successfully detect large variations in the process mean (Human et al (2010)). However, a key disadvantage is that Shewhart charts only use the most recent measurement and thus does not have a strong memory of the process history.…”

Section: Ewma Statistical Control Schemementioning

confidence: 99%

PLS-based EWMA fault detection strategy for process monitoring

Harrou

Nounou

et al. 2015

Journal of Loss Prevention in the Process Industries

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“…Shewhart control charts, were the first method proposed for testing the hypothesis and can be effectively used to detect large shifts in the process mean [26]. However, a key disadvantage of Shewhart charts is that they only use the last data sample from an inspected process and do not retain any memory of the previous data (see Figure 1).…”

Section: Spc Overviewmentioning

confidence: 99%

Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems

et al. 2016

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Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart.

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Shewhart-type control charts for variation in phase I data analysis

Cited by 26 publications

References 11 publications

The Phase I Dispersion Charts for Bivariate Process Monitoring

The Phase I Dispersion Charts for Bivariate Process Monitoring

PLS-based EWMA fault detection strategy for process monitoring

Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems

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