New fuzzy EWMA control charts for monitoring phase II fuzzy profiles

Moghadam, Ghazale; Ali, Gholam; Ardali, Raissi; Amirzadeh, Vahid

doi:10.5267/j.dsl.2015.7.004

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…In this paper is to analysis the using the fuzzy FEWMA and fuzzy X ̿ -Regression control chart for the chemical analysis data for one of the components of water is Total Dissolved Solid (TDS) from KANY factory, the data contain three groups(TDS-a, TDS-b, TDS-c) , the data for 24 days each days contain 5 hours, as shows in table (1). For 24 samples at shows in table (5), from the table (6) shows that there are (6) from ( 24) points are out of control and as shows in Fig (5).…”

Section: Application: Numerical Illustrationmentioning

confidence: 93%

FEWMA and Fuzzy Regression Model Control Chart one a-cut With Application

Rashid

Haydar

2022

ACAD J NAWROZ UNIV

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Abstract: In many real-world applications, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties, In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justiﬁed. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. As well as The fuzzy regression control chart which is a functional technique to evaluate the process in which the average has a trend and the data represents a linguistic or approximate value. In this paper, the application of fuzzy logic in statistical quality control have been done by plotting fuzzy EWMA chart and Fuzzy linear regression model control chart depending on a suggested algorithm prepared for this purpose, as well as the theoretical structure of the “a-level fuzzy midrange for a-cut fuzzy -regression control chart” is proposed for triangular membership functions and applying that to the chemical analysis data of a water component, Total Dissolved Solid ( TDS) in water from the KANY Factory to detect small shifts in the process means, the data contains three groups (TDS-a, TDS-b, TDS-c), for 24 days each day containing 5 hour data, as shown in Table (1) The comparison showed that the fuzzy linear regression model Control chart is a good technique and more suitable, accuracy, sensitivity than the traditional linear regression model Control chart.

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Section: Application: Numerical Illustrationmentioning

confidence: 93%

FEWMA and Fuzzy Regression Model Control Chart one a-cut With Application

Rashid

Haydar

2022

ACAD J NAWROZ UNIV

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“…Hence, quality control techniques, especially Statistical Process Control, have absorbed significant amount of attention as an effective tool in reducing variability of processes and improving quality [2]. Quality control (QC) is a set of planned activities which include procedures and tests to achieve a specific specification of final products [3].…”

Section: Introductionmentioning

confidence: 99%

“…table(1) .Hence by applying the mathematical models and formulas mentioned above the Arithmetic Mean and Standard deviation for Fuzzy Data where calculated and by using Excel software13. The condition of process control for each sample can be identified by the condition rule mentioned above to detect the process control of the sample as shown in table(2).…”

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confidence: 99%

Quality Improvement of Petroleum Products Using Fuzzy Control Charts

Abdulghafour

Omran

Ghulam

et al. 2017

International Journal of Natural Sciences Research

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Quality improvement is one of the most important requirements to strengthen a competitive position in our markets today. So improving the quality, will lead to decrease variations, shrinkages and so production costs hence the customers acquire the appropriate products and services to use. Control charts have an effective usage field to keep the process under control. Control charts are illustrated as graphical analysis method which defines the products whether to stay in the acceptable limits or not and as a graphical analysis technique that specifies a signal in the state of product to be out of that limits. In this paper by detecting basic concept and essentials beyond the control charts usage and the improvement; so it combined with fuzzy approach to detect the optimal limits. Hence the application of the proposed fuzzy control chart is in Al-Dura Refinery to monitor variable quality characteristics. The proposed fuzzy control chart is under vague, imprecise, uncertain, and incomplete data and based on α-level fuzzy midrange for α-cut approach. As a result of the application, it's rational to say that constructing fuzzy control charts have a more flexible, a more convenient mathematical characterization concept and have more reasonable results than the traditional quality control chart techniques.

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“…However, this method cannot be used to predict other types of requirements. Moghadama et al [6] proposed a new fuzzy EWMA (Exponentially Weighted Moving Average) control chart for fuzzy profile monitoring, which provides us with the possibility of recognizing the causes of process transition from stable mode, so that these causes may be removed and the process stable mode can be restored. However, the setting of fuzzy sets is too subjective to be used in the prediction practice.…”

Section: Introductionmentioning

confidence: 99%

Method of Spare Parts Prediction Models Evaluation Based on Grey Comprehensive Correlation Degree and Association Rules Mining: A Case Study in Aviation

Wang

Pan

Wang

et al. 2018

Mathematical Problems in Engineering

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Probability of spare parts sufficiency is crucial in the process of the normal operation of businesses, especially for the airline company. However, higher support sufficiency could inevitably lead to the increase of inventory cost of spare parts and restrict a company's efficiency. Therefore, it is important for businesses to reduce material cost on the premise of normal operation in order to accurately predict spare parts requirements based on reasonable models. The purpose of this paper is to solve problems with the evaluation of spare parts prediction models and to improve efficiency of company. Firstly, this paper summarizes a series of prediction models of spare parts requirements and applies the grey comprehensive correlation degree to rank the models. Secondly, the method of association rules mining is used to discover the association relationships between the types of spare parts and the prediction models. Finally, a case study in aviation is given to demonstrate the feasibility of the methodology, and optimal prediction models are recommended for aircraft spare parts. In accordance with the association relationships, the applicable prediction model can be provided in terms of different types of spare parts. This model will greatly enhance the work efficiency of spare parts prediction and improve the prediction tasks for the aircraft companies.

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New fuzzy EWMA control charts for monitoring phase II fuzzy profiles

Cited by 11 publications

References 12 publications

FEWMA and Fuzzy Regression Model Control Chart one a-cut With Application

FEWMA and Fuzzy Regression Model Control Chart one a-cut With Application

Quality Improvement of Petroleum Products Using Fuzzy Control Charts

Method of Spare Parts Prediction Models Evaluation Based on Grey Comprehensive Correlation Degree and Association Rules Mining: A Case Study in Aviation

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