The Ranking Function for Solving the Fuzzy Hotelling T2 Test

Quality Control Chart ◽

Triangular Membership Function ◽

A multivariate control chart is measured by many variables that are correlated in production, using the quality characteristics in any product. In this paper, statistical procedures were employed to find the multivariate quality control chart by utilizing fuzzy Hotelling test. The procedure utilizes the triangular membership function to treat the real data, which were collected from Baghdad Soft Drinks Company in Iraq. The quality of production was evaluated by using a new method of the ranking function.

QUALITY EVALUATION USING GEOMETRIC DISTANCE APPROACH

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539399000140 ◽

1999 ◽

Vol 06 (02) ◽

pp. 139-153 ◽

Cited By ~ 12

Author(s):

FU-KWUN WANG ◽

NORMA F. HUBELE

Keyword(s):

Control Variable ◽

Real Data ◽

Statistical Technique ◽

Quality Characteristics ◽

Process Data ◽

Multivariate Statistical ◽

Geometric Distance ◽

Multivariate Quality Control ◽

Performance Metric ◽

Quantitatively assessing quality and using this assessment for competitive benchmarking and diagnostics of manufactured part failure are very important for continuous improvement in modern manufacturing industries. Process capability analysis often entails characterizing or assessing process specification or quality characteristics. When these quality characteristics are related, the analysis should be based on a multivariate statistical technique. A current problem in multivariate quality control is that there is no consensus about a methodology for assessing capability. Thus, the critical first step in instituting a multivariate control scheme is not well defined. While numerous authors have recently proposed alternative definitions of multivariate capability indices, those methods may not be practical in some cases. In this research, a new process control variable, geometric distance (GD), for assessing or evaluating the quality of manufactured product is developed and investigated for reducing dimensionality. The theoretical distribution of the geometric distance is investigated and a suitable performance metric of the multivariate process data is proposed. Finally, some real data are used to demonstrate the capability of the proposed method.

Multivariate control chart based on PCA mix for variable and attribute quality characteristics

Production & Manufacturing Research ◽

10.1080/21693277.2018.1517055 ◽

2018 ◽

Vol 6 (1) ◽

pp. 364-384 ◽

Cited By ~ 7

Author(s):

Muhammad Ahsan ◽

Muhammad Mashuri ◽

Heri Kuswanto ◽

Dedy Dwi Prastyo ◽

Hidayatul Khusna

Keyword(s):

Control Chart ◽

Quality Characteristics ◽

Attribute Quality ◽

A Multivariate Control Chart for Monitoring Several Exponential Quality Characteristics Using EWMA

IEEE Access ◽

10.1109/access.2018.2881054 ◽

2018 ◽

Vol 6 ◽

pp. 70349-70358 ◽

Cited By ~ 4

Author(s):

Nasrullah Khan ◽

Muhammad Aslam ◽

Mansour Sattam Aldosari ◽

Chi-Hyuck Jun

Keyword(s):

Control Chart ◽

Quality Characteristics ◽

A new multivariate control chart for monitoring the quality of a process with the aid of auxiliary information

Journal of Statistical Computation and Simulation ◽

10.1080/00949655.2021.1969571 ◽

2021 ◽

pp. 1-22

Author(s):

Jyun-You Chiang ◽

Tzong-Ru Tsai ◽

Hoang Pham ◽

Kaizhi Yu

Keyword(s):

Control Chart ◽

Auxiliary Information ◽

Improving Phase I Monitoring of Dirichlet Regression Profiles

International Journal of Reliability Quality and Safety Engineering ◽

10.1142/s0218539318500304 ◽

2018 ◽

Vol 25 (06) ◽

pp. 1850030 ◽

Cited By ~ 1

Author(s):

Hourieh Foroutan ◽

Amirhossein Amiri ◽

Reza Kamranrad

Keyword(s):

Phase I ◽

Control Chart ◽

Control Charts ◽

Compositional Data ◽

Ratio Test ◽

Statistical Process ◽

Explanatory Variables ◽

Monitoring Procedure ◽

In most statistical process control (SPC) applications, quality of a process or product is monitored by univariate or multivariate control charts. However, sometimes a functional relationship between a response variable and one or more explanatory variables is established and monitored over time. This relationship is called “profile” in SPC literature. In this paper, we specifically consider processes with compositional data responses, including multivariate positive observations summing to one. The relationship between compositional data responses and explanatory variables is modeled by a Dirichlet regression profile. We develop a monitoring procedure based on likelihood ratio test (lrt) for Phase I monitoring of Dirichlet regression profiles. Then, we compare the performance of the proposed method with the best method in the literature in terms of probability of signal. The results of simulation studies show that the proposed control chart has better performance in Phase I monitoring than the competing control chart. Moreover, the proposed method is able to estimate the real time of a change as well. The performance of this feature is also investigated through simulation runs which show the satisfactory performance. Finally, the application of the proposed method is illustrated based on a real case in comparison with the existing method.

Design multivariate statistical process control procedure in the case of Ethio cement

International Journal of Quality & Reliability Management ◽

10.1108/ijqrm-07-2021-0227 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Daniel Ashagrie Tegegne ◽

Daniel Kitaw Azene ◽

Eshetie Berhan Atanaw

Keyword(s):

Neural Network ◽

Production Process ◽

Control Chart ◽

Cement Industry ◽

Time Interval ◽

Control Procedure ◽

Sufficient Information ◽

Content Type ◽

PurposeThis study aims to design a multivariate control chart that improves the applicability of the traditional Hotelling T2 chart. This new type of multivariate control chart displays sufficient information about the states and relationships of the variables in the production process. It is used to make better quality control decisions during the production process.Design/methodology/approachMultivariate data are collected at an equal time interval and are represented by nodes of the graph. The edges connecting the nodes represent the sequence of operation. Each node is plotted on the control chart based on their Hotelling T2 statistical distance. The changing behavior of each pair of input and output nodes is studied by the neural network. A case study from the cement industry is conducted to validate the control chart.FindingsThe finding of this paper is that the points and lines in the classic Hotelling T2 chart are effectively substituted by nodes and edges of the graph respectively. Nodes and edges have dimension and color and represent several attributes. As a result, this control chart displays much more information than the traditional Hotelling T2 control chart. The pattern of the plot represents whether the process is normal or not. The effect of the sequence of operation is visible in the control chart. The frequency of the happening of nodes is recognized by the size of nodes. The decision to change the product feature is assisted by finding the shortest path between nodes. Moreover, consecutive nodes have different behaviors, and that behavior change is recognized by neural network.Originality/valueModifying the classical Hotelling T2 control chart by integrating with the concept of graph theory and neural network is new of its kind.