Adaptive risk adjusted exponentially weighted moving average control chart based on accelerated failure time regression

Asif, Faiza; Noor‐ul‐Amin, Muhammad

doi:10.1002/qre.3195

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…To evaluate a therapy process, Kazemi et al [22] designed a risk-adjusted multivariate Tukey's CUSUM control chart. Asif and Noor-ul Amin [23] applied the idea of an adaptive EWMA control chart in their study and discussed a novel adaptive risk-adjusted EWMA control chart by utilizing the AFT regression. Vasconcelos et al [24] developed a control chart with Weibull-distributed quality characteristics for monitoring a process mean.…”

Section: Introductionmentioning

confidence: 99%

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

et al. 2023

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Product reliability is a crucial component of the industrial production process. Several statistical process control techniques have been successfully employed in industrial manufacturing processes to observe changes in reliability-related quality variables. These methods, however, are only applicable to single-stage processes. In reality, manufacturing processes consist of several stages, and the quality variable of the previous stages influences the quality of the present stage. This interdependence between the stages of a multistage process is an important characteristic that must be taken into account in process monitoring. In addition, sometimes datasets contain outliers and consequently, the analysis produces biased results. This study discusses the issue of monitoring reliability data with outliers. To this end, a proportional hazard model has been assumed to model the relationship between the significant quality variables of a two-stage dependent manufacturingprocess. Robust regression technique known as the M-estimation has been implemented to lessen the effect of outliers present in the dataset corresponding to reliability-related quality characteristics in the second stage of the process assuming Nadarajah and Haghighi distribution. The three monitoring approaches, namely, one lower-sided cumulative sum and two one-sided exponentially weighted moving average control charts have been designed to effectively monitor the two-stage dependent process. Using Monte Carlo simulations, the efficiency of the suggested monitoring schemes has been examined. Finally, two real-world examples of the proposed control approaches are provided in the study.

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Section: Introductionmentioning

confidence: 99%

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

et al. 2023

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Risk adjusted EWMA control chart based on support vector machine with application to cardiac surgery data

Noor-ul-Amin,

Khan,

Alzahrani

et al. 2024

Sci Rep

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In the current study, we demonstrate the use of a quality framework to review the process for improving the quality and safety of the patient in the health care department. The researchers paid attention to assessing the performance of the health care service, where the data is usually heterogeneous to patient’s health conditions. In our study, the support vector machine (SVM) regression model is used to handle the challenge of adjusting the risk factors attached to the patients. Further, the design of exponentially weighted moving average (EWMA) control charts is proposed based on the residuals obtained through SVM regression model. Analyzing real cardiac surgery patient data, we employed the SVM method to gauge patient condition. The resulting SVM-EWMA chart, fashioned via SVM modeling, revealed superior shift detection capabilities and demonstrated enhanced efficacy compared to the risk-adjusted EWMA control chart.

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Adaptive risk adjusted exponentially weighted moving average control chart based on accelerated failure time regression

Cited by 2 publications

References 21 publications

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

Robust Surveillance Schemes Based on Proportional Hazard Model for Monitoring Reliability Data

Risk adjusted EWMA control chart based on support vector machine with application to cardiac surgery data

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