Generalization of the Run Rules for the Shewhart Control Charts

Yasui, Seiichi; Ojima, Yoshikazu; Suzuki, Tomomichi

doi:10.1007/3-7908-1687-6_13

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Koutras et al [9] presented the subject of Shewhart control charts that are supplemented with additional rules. Yasui et al [10] introduced two additional run rules. According to their work, a process might be considered out of control if 1.…”

Section: Introductionmentioning

confidence: 99%

Detecting flow meter drift by using artificial neural networks

Salamah¹,

Palaneeswaran²,

Savsar³

et al. 2011

Int. J. SDP

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In this paper, artifi cial neural networks (ANNs) were used to assess the performance of fl ow meters used in industrial water supply. These fl ow meters are susceptible to drift, a condition causing them to give erroneous readings that are inconsistent with the actual fl ow. A simulation of industrial water fl ow to the industrial consumers was made. This simulation contained both healthy and drifting fl ow meter readings. ANN was built and trained on the simulated data. At the time of testing, the ANN developed was correct 89.52% of the time in determining the status of the fl ow recorded by a fl ow meter. Keywords: artifi cial neural network, fl ow meter drift, industrial water supply, statistical process control. INTRODUCTIONThe importance of water cannot be overemphasized. Because of this, people realized the importance of controlling, organizing, and regulating its use. No sensible controlling, organizing, regulating, and planning can be done without accurate measurement. Hence, the abundance of fl ow meters in any water system. Accurate measurement arrangements are required for effectively managing water infrastructure regimes. Flow meters can be subject to specifi c failures such as erroneous readings and breakdown. Inaccurate measurements affect the controlling, organizing, and regulating processes of the water system and planning. Measures for avoiding and mitigating such fl ow meter problems include: calibration, repair, and replacement. Flow meter calibration generally involves checking against accurate standards to determine any appreciable deviations and correcting for errors. Yet, this calibration is not a one time transaction as it does not end the problem forever.Flow meter calibration is often neglected by the operation and maintenance staff of a water system. Drift is an error in measurement, which can increase with time. Ben Salamah et al.[1] outlined a statistical process control (SPC)-based fl ow meter drift detection method for the industrial water use regime. Realizing the usefulness of artifi cial neural network (ANN) modeling for forecasting and classifi cation objectives, an extension of this research considered specifi c applications of ANN modeling with improved SPC frameworks for fl ow meters. This paper presents some key fi ndings from the application of ANN models for detecting fl ow meter drifts.

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Section: Introductionmentioning

confidence: 99%

Detecting flow meter drift by using artificial neural networks

Salamah¹,

Palaneeswaran²,

Savsar³

et al. 2011

Int. J. SDP

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Run Rules median control charts for monitoring process mean in manufacturing

Tran

2017

Quality & Reliability Eng

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Recent studies show that Shewhart median ( trueX˜) chart is simpler than the Shewhart trueX¯ chart and it is robust against outliers, but it is often rather inefficient in detecting small or moderate process shifts. The statistical sensitivity of a Shewhart control chart can be improved by using supplementary Run Rules. In this paper, we propose the Phase II median Run Rules type control charts. A Markov chain methodology is used to evaluate the statistical performance of these charts. Moreover, the performance of proposed charts is investigated in the presence of a measurement errors and modelled by a linear covariate error model. An extensive numerical analysis with several tables and figures to show the statistical performance of the investigated charts is provided for both cases of measurement errors and no measurement errors. An example illustrates the use of these charts.

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Monitoring the ratio of two normal variables using Run Rules type control charts

Tran

Castagliola

Celano

2015

International Journal of Production Research

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Recent studies show that Shewhart-type control charts monitoring the ratio of two normal random variables are useful to perform continuous surveillance in several manufacturing environments; anyway, they have a poor statistical sensitivity in the detection of small or moderate process shifts. The statistical sensitivity of a Shewhart control chart can be improved by implementing supplementary Run Rules. In this paper, we investigate the performance of Phase II Run Rules Shewhart control charts monitoring the ratio with each subgroup consisting of n > 1 sample units. A Markov chain methodology coupled with an efficient normal approximation of the ratio distribution is used to evaluate the statistical performance of these charts. We provide an extensive numerical analysis consisting of several tables and figures to discuss the statistical performance of the investigated charts for deterministic and random shift sizes affecting the in-control ratio. An illustrative example from the food industry is provided for illustration.

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Generalization of the Run Rules for the Shewhart Control Charts

Cited by 4 publications

References 6 publications

Detecting flow meter drift by using artificial neural networks

Detecting flow meter drift by using artificial neural networks

Run Rules median control charts for monitoring process mean in manufacturing

Monitoring the ratio of two normal variables using Run Rules type control charts

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