scholarly journals Product Reliability Oriented Design Scheme of Control Chart Based on the Convergent CEV for Censored Characteristics

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yihai He ◽  
Linbo Wang ◽  
Zhenzhen He ◽  
Yi Wei
Keyword(s):  
Control Chart ◽  
Manufacturing Process ◽  
Control Charts ◽  
Performance Indicator ◽  
Product Reliability ◽  
Design Procedures ◽  
Practical Performance ◽  
Asymptotic Reduction ◽  
Novel Design

Since the censored characteristics are unmonitored effectively in the manufacturing process, the produced product tends to have an unexpected high infant failure rate. Thus, this paper analyzes the associated relationship between product reliability and censored quality characteristics in manufacturing process firstly, and the dynamic and universal control demands for censored characteristics are presented. In view that traditional CEV-based control charts are usually confined to some specific types when dealing with censored characteristics, which greatly restrict the wide application of censored control charts in the high-quality manufacturing process, the convergent CEV technique based on the principle of asymptotic reduction is proposed to be better applied in the analysis of censored characteristics. And novel design procedures and simple but practical performance indicator in the form of the most dangerous alarm distance for censored characteristics of Weibull distribution are put forward. Finally, the validity of the proposed method is verified by a case study of monitoring a censored characteristic relative to lifetime of some aeronautical bearings, and the result proves that the sensitivity of the proposed control chart increased 79.96% compared to traditional CEV chart and is proved to be applicable to monitoring mixed censored characteristics affecting product reliability in manufacturing process.

SURVEILLANCE OF DIABETES PREVALENCE RATE THROUGH THE DEVELOPMENT OF A MARKOV-BASED CONTROL CHART

2012 ◽  
Vol 12 (04) ◽  
pp. 1250083
Author(s):  
PERSHANG DOKOUHAKI ◽  
RASSOUL NOOROSSANA
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
High Performance ◽  
Discrete Data ◽  
Correlated Data ◽  
Statistical Process ◽  
Control Charting ◽  
Attribute Quality ◽  
Related Process

In the field of statistical process control (SPC), usually two issues are addressed; the variables and the attribute quality characteristics control charting. Focusing on discrete data generated from a process to be monitored, attributes control charts would be useful. The discrete data could be classified into two categories; the independent and auto-correlated data. Regarding the independence in the sequence of discrete data, the typical Shewhart-based control charts, such as p-chart and np-chart would be effective enough to monitor the related process. But considering auto-correlation in the sequence of the data, such control charts would not workanymore. In this paper, considering the auto-correlated sequence of X1, X2,…, Xt,… as the sequence of zeros or ones, we have developed a control chart based on a two-state Markov model. This control chart is compared with the previously developed charts in terms of the average number of observations (ANOS) measure. In addition, a case study related to the diabetic people is investigated to demonstrate the applicability and high performance of the developed chart.

The contribution of statistical processes in the control of technological processes

2021 ◽  
Vol 66 (1) ◽  
pp. 5-16
Author(s):  
Olga-Ioana Amariei ◽  
Codruța-Oana Hamat ◽  
Alexandru-Victor Amariei
Keyword(s):  
Quality Control ◽  
Control Chart ◽  
Manufacturing Process ◽  
Quality Characteristic ◽  
Screw Head ◽  
Technological Processes ◽  
Quality Control Chart ◽  
The Way

In this paper, a manufacturing process is analyzed, having as quality characteristic the “height of the screw head”, using analyzes and representative diagrams. Based on this case study, the way to solve these types of problems using the Quality Control Chart module of the WinQSB program, as well as the XLSTAT program is presented.

Measurement System Analysis Combined with Shewhart’s Approach

2015 ◽  
Vol 637 ◽  
pp. 7-11 ◽  
Author(s):  
Magdalena Diering ◽  
Adam Hamrol ◽  
Agnieszka Kujawińska
Keyword(s):  
Process Control ◽  
Control Chart ◽  
Measurement System ◽  
Manufacturing Process ◽  
Control Charts ◽  
System Analysis ◽  
Standard Procedure ◽  
Reference Value ◽  
Measurement Systems ◽  
On Line

The paper presents new procedure of methodology for statistical assessment of measurement systems variation (methodology known in the literature as Measurement Systems Analysis, MSA). This procedure allows for calculation and monitoring in real time (that is on-line) of measurement system (MS) characteristics which determine its usability for manufacturing process control. The presented solution pointed out the gap in process control, which consists in lack of methods for monitoring measurement processes in the on-line way. Their key point consists of taking samples that are also needed for the process control chart for the needs of the MSA method. This means that the samples are taken directly from the production line and during the production process. The method is combined with the standard procedure of statistical process control (SPC) with the use of process control charts. It is based on two control charts. The first one is called AD-chart (Average Difference chart) and it allows to estimate the variation between the operators and stability of the monitored measurement system. The second control chart illustrates the %R&R index (Repeatability and Reproducibility) and allows to monitor the MS capability.The paper also presents authors’ proposal of guidelines about the reference value for the %R&R index calculation and assessment. Recommendations and guidelines for choosing the reference value are based on two criteria: information about sample and manufacturing process variation and the purpose of using MS (product or process control).

scholarly journals Control Chart in the Service Industry: A Case Study in a University Health Clinic

2020 ◽  
Vol 1 (4) ◽  
pp. 436-441
Author(s):  
Johan K. Runtuk
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Processing Time ◽  
Service Industry ◽  
Services Quality ◽  
Service Level ◽  
Health Clinic ◽  
Capability Analysis

This study analyzes the service quality of a University health clinic in Bekasi, WestJava, Indonesia. A control chart and capability analysis will be employed to analyzethe services' quality, especially registration processing time and the drug defect permonth. This study uses the CUSUM and EWMA control charts for detecting thesmall shifts in the processing time. The findings revealed that the current processesare in control and capable of meeting the current service level. There is only onepoint outside the upper limit. But it can be ignored/deleted since the assignablecause causes that event. This study gives direction for the Clinic to improve theservice quality.

scholarly journals Monitoring the Variability in the Process Using Neutrosophic Statistical Interval Method

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 562 ◽  
Author(s):  
Muhammad Aslam ◽  
Nasrullah Khan ◽  
Muhammad Khan
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Interval Methods ◽  
Interval Method ◽  
Classical Statistics ◽  
Neutrosophic Statistics ◽  
Imprecise Observations ◽  
Better Than

Existing variance control charts are designed under the assumptions that no uncertain, fuzzy and imprecise observations or parameters are in the population or the sample. Neutrosophic statistics, which is the extension of classical statistics, has been widely used when there is uncertainty in the data. In this paper, we will originally design S 2 control chart under the neutrosophic interval methods. The complete structure of the neutrosophic S 2 control chart will be given. The necessary measures of neutrosophic S 2 will be given. The neutrosophic coefficient of S 2 control chart will be determined through the neutrosophic algorithm. Some tables are given for practical use. The efficiency of the proposed control chart is shown over the S 2 control chart designed under the classical statistics in neutrosophic average run length (NARL). A real example is also added to illustrate the proposed control chart. From the comparison in the simulation study and case study, it is concluded that the proposed control chart performs better than the existing control chart under uncertainty.

scholarly journals Statistical Development of the V S Q -Control Chart for Extreme Data with an Application to the Carbon Fiber Industry

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Faisal Shah ◽  
Zahid Khan ◽  
Muhammad Aslam ◽  
Seifedine Kadry
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Statistical Power ◽  
Average Run Length ◽  
Probability Model ◽  
Maxwell Model ◽  
Performance Measure ◽  
Underlying Assumption ◽  
Novel Design ◽  
Statistical Development

The normality assumption is a significant part of the development of control charts. This underlying assumption of normality most likely does not hold true in real scenarios. One of such designs usually devised to observe the target parameter σ 2 of the Maxwell quality characteristics is the V -control chart. In general, quality practitioners preferably have to observe the scale parameter σ rather than σ 2 in examined processes. The contemporary V -control chart is relying on the V -statistic which does not hold the unbiasedness property with respective to parameter σ of the Maxwell probability model. In view of this, implementation of the V -chart is not an appropriate design in monitoring a real parameter of the underlying Maxwell data. To accommodate the monitoring of the parameter σ of the Maxwell model, a novel design of the V S Q -chart is mainly proposed in this work. To support a statistical understanding of the V S Q -chart, power function, characteristic function, and the average run length ARL have been essentially established. The parameters of the V S Q -chart are determined from the results of the sampling distribution of the derived statistic. Analytical findings are further applied to determine the performance of the study proposal with its existing counterpart. Substantially, the better performance of the proposed technique has been observed because of statistical power used as a performance measure. Eventually, the computational plan of the V S Q -chart is considered both for the simulated and real datasets with the aim of illustrating the theory of the proposed design.

Development of fuzzy U control chart for monitoring defects

2014 ◽  
Vol 31 (7) ◽  
pp. 811-821 ◽  
Author(s):  
Soroush Avakh Darestani ◽  
Azam Moradi Tadi ◽  
Somayeh Taheri ◽  
Maryam Raeiszadeh
Keyword(s):  
Control Chart ◽  
Fuzzy Number ◽  
Control Charts ◽  
Statistical Process ◽  
Content Type ◽  
Trapezoidal Fuzzy Number ◽  
Defect Sensitivity ◽  
Control Patterns ◽  
Definition Of

Purpose – Shewhart's control charts are the most important statistical process control tools that play a role in inspecting and producing quality control. The purpose of this paper is to investigate the attributes of fuzzy U control chart. Design/methodology/approach – If the data were uncertain, they were converted into trapezoidal fuzzy number and the fuzzy upper and lower control limits were trapezoidal fuzzy number calculated using fuzzy mode approach. The result was grouped into four categories (in control, out of control, rather in control, rather out of control). Finally, a case study was presented and the method coding was done in MATLAB software using design U control chart; then, the results were verified. Findings – The definition of fuzzy numbers for each type of defect sensitivity and the unit can be classified into four groups: in-control and out-of-control, rather in-control and rather out-of-control which represent the actual quality of the products. It can be concluded that fuzzy control chart is more sensitive on recognition out of control patterns. Originality/value – This paper studies the use of control charts, specifically the attributes of a fuzzy U control chart, for monitoring defects in the format of a case study.

scholarly journals Multivariate Control Chart and Lee–Carter Models to Study Mortality Changes

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2093
Author(s):  
Gisou Díaz-Rojo ◽  
Ana Debón ◽  
Jaime Mosquera
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Causes Of Death ◽  
Mortality Model ◽  
Probability Of Death ◽  
Multivariate Control Chart ◽  
Model Residuals ◽  
Multivariate Control ◽  
Carter Model

The mortality structure of a population usually reflects the economic and social development of the country. The purpose of this study was to identify moments in time and age intervals at which the observed probability of death is substantially different from the pattern of mortality for a studied period. Therefore, a mortality model was fitted to decompose the historical pattern of mortality. The model residuals were monitored by the T2 multivariate control chart to detect substantial changes in mortality that were not identified by the model. The abridged life tables for Colombia in the period 1973–2005 were used as a case study. The Lee–Carter model collects information regarding violence in Colombia. Therefore, the years identified as out-of-control in the charts are associated with very early or quite advanced ages of death and are inversely related to the violence that did not claim as many victims at those ages. The mortality changes identified in the control charts pertain to changes in the population’s health conditions or new causes of death such as COVID-19 in the coming years. The proposed methodology is generalizable to other countries, especially developing countries.

scholarly journals MULTIVARIATE CONTROL CHARTS BASED ON DATA DEPTH FOR SUBGROUP LOCATION AND SCALE - CASE STUDY

2018 ◽  
Vol 6 ◽  
pp. 1042-1049
Author(s):  
Izabela D. Czabak-Górska
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Quality Characteristics ◽  
Data Depth ◽  
Multivariate Control Charts ◽  
Production Practice ◽  
Classic Approach ◽  
Multivariate Control ◽  
T2 Control Chart

The purpose of the article is to present a method for determining control charts, which allow to control few interrelated quality characteristics. Often, in production practice, there is a need to simultaneously control several interrelated quality characteristics. The use of univariate control charts, separately for each quality characteristics, may lead to inadequate corrective actions of a production process. In such situations, it is recommended to use multivariate control charts, for example, the T2 control chart. However, the use of this classic approach involves making complicated calculations. Therefore, the author of this paper suggests using multivariate control charts based on data depth proposed by Liu.  In this paper, the author presented the idea and principles of the multivariate control chart based on data depth and then, using it to assess the statistical stability of the process in a manufacturing company, engaged in the production of window fittings.

The influence of currents on circular secondary clarifier performance and design

1996 ◽  
Vol 34 (3-4) ◽  
pp. 405-412 ◽  
Author(s):  
Andrea Deininger ◽  
Frank W. Günthert ◽  
Peter A. Wilderer
Keyword(s):  
Wastewater Treatment ◽  
Density Currents ◽  
Clear Water ◽  
Performance Measurements ◽  
Design Procedures ◽  
Secondary Clarifier ◽  
Current Design ◽  
Hydraulic Load ◽  
Novel Design ◽  
Wastewater Treatment Systems

Density currents in the deeper zones of clarifiers and currents in the clear water zone have a significant influence on clarifier performance. Measurements of flow velocity profiles were conducted in full-scale horizontally flown circular secondary clarifiers. Relations between the hydraulic load and the development of density currents could be detected. Those patterns are not taken into account in current design procedures. Stationary design approaches are mainly based on the overflow rate. Novel design methods based on the dynamic behavior of flow and density distribution in clarifiers are needed in order to improve the efficacy of wastewater treatment systems.

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