A Fuzzy Bivariate Poisson Control Chart

Wibawati,; Mashuri, Muhammad; Purhadi, Purhadi; Irhamah,

doi:10.3390/sym12040573

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…A synthetic control chart for attribute inspection is developed by Zhou, Liu, and Zheng 19 . Mashuri et al 20 proposed fuzzy bivariate for monitoring the Poisson process. The attribute chart for the joint monitoring of mean and variance is presented by Quinino et al 21 .…”

Section: Introductionmentioning

confidence: 99%

Comparing the performance of Kernel PCA Mix Chart with PCA Mix Chart for monitoring mixed quality characteristics

Ahsan

Mashuri

Khusna

2022

Sci Rep

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Along with the development of information and technology, the quality characteristics of a product cannot be monitored separately in the different types of control charts. In the past, conventional control charts were developed to monitor only one type of quality characteristic. The variable control charts are used to observe the variable or metric quality characteristics. Meanwhile, in monitoring non-metric characteristics or categorical data, attribute control charts are employed. To accommodate these two types of data, the PCA Mix control chart is suggested to simultaneously monitor these two types of data in one chart. However, some drawbacks occur when this chart is applied to monitor non-metric data which has an imbalanced proportion. Therefore, the Kernel PCA Mix control chart is created to overcome the gaps that occurred in the PCA Mix chart. Similar to the previous chart, this chart is also constructed using Hotelling’s T2 statistics with Kernel Density Estimation control limit. Several simulations are used to evaluate the performance of the proposed control charts. The simulation results show that the proposed chart has a better result than the previous control chart, especially for a small mean shift with an imbalanced proportion of non-metric data. However, the PCA Mix chart has a similar performance to the proposed chart when it is applied to monitor the balanced proportion of categorical data with a large mean shift. The application with simulated data with various scenarios and the real-world case also shows that the Kernel PCA Mix chart performs better compared to the performance of the PCA Mix chart.

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Section: Introductionmentioning

confidence: 99%

Comparing the performance of Kernel PCA Mix Chart with PCA Mix Chart for monitoring mixed quality characteristics

Ahsan

Mashuri

Khusna

2022

Sci Rep

Self Cite

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“…Gubay and Kahraman [6,7] proposed using the percentage of sample fuzzy data points falling into the control limit to determine whether the process is in control. Wibawati et al [8] proposed a kind of fuzzy binary Poisson (FBP) control chart based on C-chart. The above mentioned literature proposed good ideas for the treatment of fuzzy multilevel quality characteristics, but their common point is that the processing and evaluation of fuzzy multilevel quality characteristics can be realized by changing control limits of the control chart, which is not sufficient for solving the current situation of dichotomy quality control with multilevel fuzzy quality characteristics.…”

Section: Introductionmentioning

confidence: 99%

Research on fuzzy control charts for fuzzy multilevel quality characteristics

Zhou

Huang

2021

Int. J. Metrol. Qual. Eng.

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Fuzzy control charts are proposed to solve the problem that traditional control charts cannot be applied to fuzzy quality characteristics. First, fuzzy quality characteristics are converted to representative statistics, which are fuzzy mode transformation, fuzzy level midrange transformation and fuzzy level median transformation. Control charts are designed based on the Poisson distribution. Second, the effects of the different statistics are analysed. Direct Fuzzy Control Charts are designed to avoid some information omission when translating fuzzy quality characteristics into representative statistics. The area ratio that falls within the control limits is used to conclude whether the process is relative out of control or in control. The performance of the control charts is analysed by MATLAB simulation. Finally, an example of an energy metre assembly is given to prove the proposed method.

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