Exponentially Weighted Moving Average Control Chart Based on Normal Fuzzy Random Variables

Hesamian, Gholamreza; Akbari, Mohammad Ghasem; Ranjbar, Elham

doi:10.1007/s40815-019-00610-4

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…The fuzzification method occurs in a similar way to the methods proposed by [23,24]. They used random variables to transform crisp numbers into fuzzy numbers.…”

Section: Fuzzification Methodsmentioning

confidence: 99%

Performance Analysis of Interval Type-2 Fuzzy X¯ and R Control Charts

Almeida,

dos Santos Mendes,

Rocha Rizol

et al. 2023

Applied Sciences

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Statistical process control (SPC) is one of the most powerful techniques for improving quality, as it is able to detect special causes of problems in processes, products and services with a remarkable degree of accuracy. Among SPC tools, X¯ and R control charts are widely employed in process monitoring. However, scenarios involving vague, imprecise and even subjective data require a type-2 fuzzy set approach. Thus, X¯ and R control charts should be coupled with interval type-2 triangular fuzzy numbers (IT2TFN) in order to add further information to traditional control charts. This paper proposes a performance analysis of IT2TFN and X¯ and R control charts by means of average run length (ARL), standard deviation of the run length (SDRL) and RL percentile. Computer simulations were carried out considering 10,000 runs to obtain ARL, SDRL and the 5th, 25th, 50th, 75th and 95th RL percentiles. Simulation results reveal that the proposed control charts increased fault detection capability (speed of response) and slightly reduced the number of false alarms in processes under control. Moreover, it was observed that, in addition to superior performance, IT2TFN X¯-R control charts proved to be more robust and flexible when compared to traditional control charts.

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“…The fuzzification method occurs in a similar way to the methods proposed by [23,24]. They used random variables to transform crisp numbers into fuzzy numbers.…”

Section: Fuzzification Methodsmentioning

confidence: 99%

Performance Analysis of Interval Type-2 Fuzzy X¯ and R Control Charts

Almeida,

dos Santos Mendes,

Rocha Rizol

et al. 2023

Applied Sciences

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“…This control chart was developed from the fast initial response exponentially weighted moving average (FIR_EWMA) control chart (Lucas and Saccucci, 1990). By considering the adjusted fast initial response feature or FIRadj values (Steiner, 1999) were presented by Steiner as shown in equation (8). In this research, the constant f is set at 0.5 and the past researches (Lucas and Saccucci, 1990;Steiner, 1999;Knoth, 2005) were found that this value is the optimal constant for obvious initial detection of process abnormalities.…”

Section: The Proposed Control Chartmentioning

confidence: 98%

New Quality Control Chart to Quickly Detect the Changes of Process Average

2021

QAS

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The objective of this article is to propose a new control chart-improved exponentially weighted moving average (IEWMA) control chart-to fast detect the mean shifts of process when quality characteristic data are normally distributed. This chart still has robust property even though its controls limits are created from data with outliers. The efficiency inspection of IEWMA control chart is managed 504 situations for the simulation data. Moreover, the four control charts, namely, exponentially weighted moving average (EWMA), robust exponentially weighted moving average (REWMA), median mean absolute deviation (MDMAD), and average control charts, are compared the performances with IEWMA control chart. All charts are constructed by using data set in two cases, i.e., the first case that data are not include outliers and the second case that data are composed of outliers. It is found that in the case of non-outliers in the data, the three charts-IEWMA, EWMA and REWMA control charts-tend to have the most capability for process average shift detection for all sample sizes and all levels of the process average changes. For the case of outliers in the data, the IEWMA control chart tends to have the most efficiency for all sample sizes, especially for the tiny process shifts from the target.

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