Distribution‐free mixed cumulative sum‐exponentially weighted moving average control charts for detecting mean shifts

Malela‐Majika, Jean‐Claude; Rapoo, Eeva Maria

doi:10.1002/qre.2162

Cited by 22 publications

(11 citation statements)

References 23 publications

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“…More information about the above overall performance measures can be found in Han and Tsung, 48 Wu et al., 49 Riaz and Abbasi, 16 Malela‐Majika and Rapoo, 39 and Shongwe et al 47 …”

Section: Comparison Studymentioning

confidence: 99%

“…However, many authors have developed nonparametric control charts based on the exceedance, Wilcoxon rank‐sum and Mann–Whitney statistics when the location parameter of an unknown continuous distribution is unknown (see, for example, Refs. 35–42). An extensive analysis of nonparametric Shewhart‐type, CUSUM, and EWMA control charts is given by Chakraborti and Graham 3,43 while an updated overview up to 2020 of nonparametrics charts can be seen in Koutras and Triantafyllou 44…”

Section: Introductionmentioning

confidence: 99%

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A nonparametric triple exponentially weighted moving average sign control chart

Alevizakos

Chatterjee

Koukouvinos

2020

Quality & Reliability Eng

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Nonparametric control charts are used in process monitoring when there is insufficient information about the form of the underlying distribution. In this article, we propose a triple exponentially weighted moving average (TEWMA) control chart based on the sign statistic for monitoring the location parameter of an unknown continuous distribution. The run‐length characteristics of the proposed chart are evaluated performing Monte Carlo simulations. We also compare its statistical performance with existing nonparametric sign charts, such as the cumulative sum (CUSUM), exponentially weighted moving average (EWMA), generally weighted moving average (GWMA), and double exponentially weighted moving average (DEWMA) sign charts as well as the parametric TEWMA‐trueX¯ chart. The results show that the TEWMA sign chart is superior to its competitors, especially for small shifts. Moreover, two examples are given to demonstrate the application of the new scheme.

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“…More information about the above overall performance measures can be found in Han and Tsung, 48 Wu et al., 49 Riaz and Abbasi, 16 Malela‐Majika and Rapoo, 39 and Shongwe et al 47 …”

Section: Comparison Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A nonparametric triple exponentially weighted moving average sign control chart

Alevizakos

Chatterjee

Koukouvinos

2020

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…Malela-Majika and Rapoo [39] suggested a control diagram of the exponentially weighted moving averagecumulative sum (EWMA-CUSUM) and a distributionfree combined control diagram of the cumulative sum exponentially weighted moving average (CUSUM-EWMA). The Wilcoxon rank sum test was the basis of the diagrams alluded to in identifying the shifts of the process mean with no distributional presumption of the basic quality process.…”

Section: Introductionmentioning

confidence: 99%

Change Point Estimation of the Stationary State in Auto Regressive Moving Average Models, Using Maximum Likelihood Estimation and Singular Value Decomposition-based Filtering

Sheikhrabori

Aminnayeri

Ayoubi

2019

IJE

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In this paper, for the first time, the subject of change point estimation has been utilized in the stationary state of auto regressive moving average (ARMA) (1, 1). In the monitoring phase, in case the features of the question pursue a time series, i.e., ARMA(1,1), on the basis of the maximum likelihood technique, an approach will be developed for the estimation of the stationary state's change point. To estimate unidentified parameters following the change point, the dynamic linear model's filtering was utilized on the basis of the singular decomposition of values. The proposed model has wide applications in several fields such as finance, stock exchange marks and rapid production. The results of simulation showed the suggested estimator's effectiveness. In addition, a real example on stock exchange market is offered to delineate the application.

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“…The monitoring phase in SHM goes through two steps, damage detection and diagnosis . In this paper, we will focus on damage detection which has an important role to keep the system safe .…”

Section: Introductionmentioning

confidence: 99%

Multivariate statistical process control-based hypothesis testing for damage detection in structural health monitoring systems

Chaabane

Mansouri

Hamida

et al. 2018

Struct Control Health Monit

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The objective of this paper is to propose a new damage detection technique based on multiscale partial least squares (MSPLS) and optimized exponentially weighted moving average (OEWMA) generalized likelihood ratio test (GLRT) to enhance monitoring of structural systems. The developed technique attempts to combine the advantages of the exponentially weighted moving average (EWMA) and GLRT charts with those of multiscale input-output model partial least square (PLS) and multi-objective optimization. The damage detection problem is addressed so that the data are first modeled using the MSPLS method and then the damages are detected using the OEWMA-GLRT chart. The idea behind the developed OEWMA-GLRT is to compute an optimal statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data and selects the EWMA parameters that minimizes the (MDR), the false alarm rate (FAR) and the average run length (ARL 1 ). This helps provide a more accurate estimation of the GLRT statistic and provide a stronger memory that enables better decision making with respect to damage detection. The performance of the developed technique is assessed and compared with PLS-based GLRT, PLS-based OEWMA, and PLS-based OEWMA-GLRT techniques using two illustrative examples, synthetic data and simulated International Association for Structural Control-American society of Civil engineers (IASC-ASCE) benchmark structure. The results demonstrate the effectiveness of the MSPLS-based OEWMA-GLRT technique over the PLS-based GLRT, PLS-based OEWMA, and PLS-based OEWMA-GLRT methods in terms of MDR, FAR, and ARL 1 values. KEYWORDS damage detection, exponentially weighted moving average, generalized likelihood ratio test, multiscale, partial least squares, structural health monitoring Struct Control Health Monit. 2019;26:e2287.wileyonlinelibrary.com/journal/stc

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Distribution‐free mixed cumulative sum‐exponentially weighted moving average control charts for detecting mean shifts

Cited by 22 publications

References 23 publications

A nonparametric triple exponentially weighted moving average sign control chart

A nonparametric triple exponentially weighted moving average sign control chart

Change Point Estimation of the Stationary State in Auto Regressive Moving Average Models, Using Maximum Likelihood Estimation and Singular Value Decomposition-based Filtering

Multivariate statistical process control-based hypothesis testing for damage detection in structural health monitoring systems

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