Phase I monitoring and change point estimation of autocorrelated poisson regression profiles

Maleki, Mohammad Reza; Amiri, Amirhossein; Taheriyoun, Ali Reza; Castagliola, Philippe

doi:10.1080/03610926.2017.1402052

Cited by 19 publications

(5 citation statements)

References 23 publications

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“…Ren et al 40 stated that the fault diagnosis with multi-channel profiles is quite challenging as compared to the simple linear profiles. Maleki et al 41 studied the “change point estimation of autocorrelated Poisson profiles” by the method of Phase-I monitoring with a condition that there exists autocorrelation among each profile’s explanatory variable. Abbas et al 42 introduced Bayesian double exponentially weighted moving average control charts for monitoring the profiles of products and processes.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of DEWMA3 in phase-II for capturing changes in simple linear profiles based on run rule mechanism

Sherwani

Qasim

Abbas

et al. 2023

Sci Rep

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In Statistical Process Control, many techniques exist for monitoring the stability of a process over time. In this work, we study the relationship of the response variable with explanatory variables in the form of linear profiles for detecting changes in slope and intercept of the linear quality profiles. We used the transformation of explanatory variables approach used for make the regression estimates independent of each other to have zero average. A comparative study of three phase-II methods using DEWMA statistics in monitoring and capturing undesirable deviations in the slope, intercept, and variability is also studied by applying different proposed run rules schemes i.e., R1/1, R2/3, R3/3. Monte Carlo simulations were carried out on R-Software for finding the results of proposed schemes by taking various levels of shifts for intercept, slope, and standard deviation in identifying the false alarm rate of a process. The simulation results based on the average run length criterion show that the proposed run rule schemes improve the detection ability of the control structure. Among all the proposed schemes R2/3 is found to be the best one because of its quick detection ability of false alarm rate. The proposed scheme also shows superiority in comparison to other schemes. The simulation results are further justified with a real data application.

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Section: Introductionmentioning

confidence: 99%

Performance evaluation of DEWMA3 in phase-II for capturing changes in simple linear profiles based on run rule mechanism

Sherwani

Qasim

Abbas

et al. 2023

Sci Rep

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“…The aforementioned control charts for Phase I and/or Phase II study of the general linear profiles are briefly summarized in Table 1. On the other hand, non-linear profile charting methods have been studied by Walker and Wright, 26 Woodall et al, 27 Williams et al, 28 Colosimo and Pacella, 29 Williams et al, 30 Yu et al, 31 Maleki et al, 32 Maleki et al, 33 Esmaeeli et al, 34 Maleki et al, 35 Fotuhi et al, 36 Menafoglio et al 37 and Khosravi and Amiri. 38 In the literature, there are also many charting methods that focus on monitoring generalized linear model profiles.…”

Section: Introductionmentioning

confidence: 99%

A new non‐parametric control chart for monitoring general linear profiles based on log‐linear modelling

Huwang

Liao

2023

Quality & Reliability Eng

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Profile monitoring has received much attention from the applications in statistical process control. It is a method for monitoring the stability of a functional relationship between a response variable and one or more explanatory variables over the time axis. General linear profiles monitoring is very important since the linear relationship between a response variable and explanatory variables is easy to characterize besides it is simple and flexible. In addition, most of general linear profiles monitoring techniques assume normality of random error variables.However, the normality of random error variables is not satisfied in certain applications. This causes the existing monitoring methods for general linear profiles both inadequate and inefficient. Based on the log-linear modelling, in this paper, we develop a non-parametric control chart for Phase II monitoring of general linear profiles where normality of random error variables is not assumed. The proposed charting method applies the CUSUM (cumulative sum) to the Pearson chi-square test for the Wilcoxon-type rank-based estimators of coefficient parameters and an estimator of random error variance. Effectiveness of the developed control chart is assessed and compared with that of two existing control charts based on the criterion of ARL (average run length). An industrial production process example is also applied to illustrate how the proposed control chart can be used in practice.

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“…Mahmood et al 41 proposed GLM-based PM and DPM strategies to monitor Poisson processes. A diverse review on GLM-based control charts can be available in Amiri et al, 42 Kuo and Chiu, 43 Maleki et al, 44 Mammadova and Özkale, 45 Marcondes Filho and Sant' Anna, 46 and Wen et al 47 The above-mentioned model-based schemes are developed under Shewhart, EWMA, CUSUM, PM, and DPM charting structures. Getting motivation from Asgari et al, 30 Alevizakos et al, 17 and Mahmood et al, 41 this study provides improved MA and DMA structures based on standardized residuals of Poisson regression model (i.e., SR-MA and SR-DMA).…”

Section: Introductionmentioning

confidence: 99%

On the improved generalized linear model‐based monitoring methods for Poisson distributed processes

Iqbal¹,

Mahmood

Nazir

et al. 2022

Concurrency and Computation

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Control charts are widely used tool that provides quality inspectors with sensitive information for maintaining manufacturing process productivity. Numerous model‐based techniques have been presented in the literature to monitor industrial operations that focus on the normal response variable. However, non‐normal response results can occur as a result of quality control operations. In such cases, a new approach based on generalized linear model that provides multiple distribution options for response variables is required to achieve better results. Therefore, this study proposes GLM‐based moving average (MA) and double moving average (DMA) schemes formed on standardized residuals derived from a fitted Poisson regression model. The productivity of suggested methods and the existing exponentially weighted moving average (EWMA) scheme is explored in terms of run length attributes. The simulation outcomes revealed that moving average schemes based on standardized residuals (i.e., SR‐MA and SR‐DMA) outperform their predecessor (i.e., SR‐EWMA). Moreover, the SR‐DMA chart, with small values of span w, has proven to be more effective at detecting minor to moderate shifts in the process mean. Finally, a case study of a 3D manufacturing operation is shown to emphasize the importance of the proposed approaches.

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Phase I monitoring and change point estimation of autocorrelated poisson regression profiles

Cited by 19 publications

References 23 publications

Performance evaluation of DEWMA3 in phase-II for capturing changes in simple linear profiles based on run rule mechanism

Performance evaluation of DEWMA3 in phase-II for capturing changes in simple linear profiles based on run rule mechanism

A new non‐parametric control chart for monitoring general linear profiles based on log‐linear modelling

On the improved generalized linear model‐based monitoring methods for Poisson distributed processes

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