Introduction to Statistical Process Control

Qiu, Peihua

doi:10.1201/b15016

Cited by 196 publications

(227 citation statements)

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“…Monte Carlo simulation method is used because of its accuracy and flexibility to handle various situations as compared with other approximation methods. 34 The desired ARL 0 is not exactly achievable becuase of the discrete nature of the signed-rank statistic SR t . However, the discrepancies are within 1% of the assumed ARL 0 .…”

Section: The Proposed Dewma-sr Chartmentioning

confidence: 99%

A new nonparametric double exponentially weighted moving average control chart

Raza

Nawaz

Aslam

et al. 2019

Quality & Reliability Eng

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There are many practical situations where the underlying distribution of the quality characteristic either deviates from normality or it is unknown. In such cases, practitioners often make use of the nonparametric control charts. In this paper, a new nonparametric double exponentially weighted moving average control chart on the basis of the signed-rank statistic is proposed for monitoring the process location. Monte Carlo simulations are carried out to obtain the run length characteristics of the proposed chart. The performance comparison of the proposed chart with the existing parametric and nonparametric control charts is made by using various performance metrics of the run length distribution. The comparison showed the superiority of the suggested chart over its existing parametric and nonparametric counterparts. An illustrative example for the practical implementation of the proposed chart is also provided by using the industrial data set.

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Section: The Proposed Dewma-sr Chartmentioning

confidence: 99%

A new nonparametric double exponentially weighted moving average control chart

Raza

Nawaz

Aslam

et al. 2019

Quality & Reliability Eng

View full text Add to dashboard Cite

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“…Figure 3 shows the results after evaluating the conditional sequential normal scores with a CUSUM chart that assumes normal standard observations. From Qiu [2013],…”

Section: Individual Observations With Known Quantile θmentioning

confidence: 99%

The sequential normal scores transformation

Conover

Tercero-Gómez

Cordero-Franco

2017

Sequential Analysis

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The sequential analysis of series often requires nonparametric procedures, where the most powerful ones frequently use rank transformations. Re-ranking the data sequence after each new observation can become too intensive computationally. This led to the idea of sequential ranks, where only the most recent observation is ranked. However, difficulties finding, or approximating, the null distribution of the statistics may have contributed to the lack of popularity of these methods. In this paper, we propose transforming the sequential ranks into sequential normal scores which are independent, and asymptotically standard normal random variables. Thus original methods based on the normality assumption may be used.A novel approach permits the inclusion of a priori information in the form of quantiles. It is developed as a strategy to increase the sensitivity of the scoring statistic. The result is a powerful convenient method to analyze nonnormal data sequences. Also, four variations of sequential normal scores are presented using examples from the literature. Researchers and practitioners might find this approach useful to develop nonparametric procedures to address new problems extending the use of parametric procedures when distributional assumptions are not met. These methods are especially useful with large data streams where efficient computational methods are required.

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“…Change detection is also the goal of statistical process control (e.g., Qiu (2013)). The recent work presented by Qiu and Xiang (2014) is similar to the method we present here.…”

Section: Related Workmentioning

confidence: 99%

Partitioning a Large Simulation as It Runs

et al. 2016

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As computer simulations continue to grow in size and complexity, they present a particularly challenging class of big data problems. at this scale can generate output that exceeds both the storage capacity and the bandwidth available for transfer to storage, making post-processing and analysis challenging. One approach is to embed some analyses in the simulation while the simulation is running -a strategy often called in situ analysis -to reduce the need for transfer to storage. Another strategy is to save only a reduced set of time steps rather than the full simulation. Typically the selected time steps are evenly spaced, where the spacing can be defined by the budget for storage and transfer. This paper combines both of these ideas to introduce an online in situ method for identifying a reduced set of time steps of the simulation to save. Our approach significantly reduces the data transfer and storage requirements, and it provides improved fidelity to the simulation to facilitate post-processing and reconstruction. We illustrate the method using a computer simulation that supported NASA's 2009 Lunar Crater Observation and Sensing Satellite mission.

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Introduction to Statistical Process Control

Cited by 196 publications

References 0 publications

A new nonparametric double exponentially weighted moving average control chart

A new nonparametric double exponentially weighted moving average control chart

The sequential normal scores transformation

Partitioning a Large Simulation as It Runs

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