scholarly journals On Designing Non-Parametric EWMA Sign Chart under Ranked Set Sampling Scheme with Application to Industrial Process

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1497
Author(s):  
Saber Ali ◽  
Zameer Abbas ◽  
Hafiz Zafar Nazir ◽  
Muhammad Riaz ◽  
Xingfa Zhang ◽  
...  
Keyword(s):  
Control Charts ◽  
Industrial Process ◽  
Ranked Set Sampling ◽  
Quality Characteristic ◽  
Sampling Scheme ◽  
False Alarms ◽  
Statistical Process ◽  
Run Length ◽  
Normality Assumption ◽  
Non Parametric

Statistical process control (SPC) tools are used for the investigation and identification of unnatural variations in the manufacturing, industrial, and service processes. The control chart, the basic and the most famous tool of SPC, is used for process monitoring. Generally, control charts are constructed under normality assumption of the quality characteristic of interest, but in practice, it is quite hard to hold the normality assumption. In such situations, parametric charts tend to offer more frequent false alarms and invalid out-of-control performance. To rectify these problems, non-parametric control charts are used, as these have the same in-control run length properties for all the continuous distributions and are known as in-control robust. This study intends to develop a new non-parametric exponentially weighted moving average (NPEWMA) chart based on sign statistics under a ranked set sampling scheme that is hereafter named (NPREWMA-SN). The run-length profiles of the NPREWMA-SN chart are computed using the Monte Carlo simulation method. The proposed scheme is compared with NPEWMA-SN and classical EWMA-X¯ charts, using different run length measures. The comparison reveals the in-control robustness and superiority of the proposed scheme over its competitors in detecting all kinds of shifts in the process location. A practical application related to the substrate manufacturing process is included to show the demonstration of the proposed chart.

scholarly journals Multivariate Statistical Quality Control Based on Ranked Set Sampling

2017 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Hakan Eygü ◽  
M. Suphi Özçomak
Keyword(s):  
Control Charts ◽  
Sampling Methods ◽  
Average Run Length ◽  
Ranked Set Sampling ◽  
Multivariate Statistical ◽  
Run Length ◽  
Hotelling's T2 ◽  
Median Ranked Set Sampling ◽  
Hotelling’S T2 ◽  
T2 Control Chart

The sample of the study was formed using simple random sampling, ranked set sampling, extreme ranked set sampling and median ranked set sampling. At the end of this process, the researcher created Hotelling’s T2 control charts, a multivariate statistical process control method. The performances of SRS, RSS, ERSS and MRSS sampling methods were compared to one another using these control charts. A simulation was performed to see the average run-length values for Hotelling’s T2 control charts, and these findings were also used for the comparison of the sampling performances.At the end of the study, the researcher formed a sample using median ranked set sampling and created the Hotelling’s T2 control chart. As a result of this operation, the researcher found that there was an out-of-control signal in the process, while there was no such signal in other sampling methods. When the average run-length values obtained from Hotelling’s T2 control charts were compared, it was seen that a shift in the process was detected by the ranked set sampling earlier, when compared to other sampling methods. This paper it can be said that the methods used are unique to the literature because they are applied to multivariate data.

An Anomaly Identification Method of Coal Gas Pre-Drainage Parameters Based on T2 Control Chart

2014 ◽  
Vol 700 ◽  
pp. 549-552
Author(s):  
Shao Jie Hou ◽  
Xian Zun Meng ◽  
Yu Wei Zhang
Keyword(s):  
Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Industrial Process ◽  
Research Field ◽  
Statistical Process ◽  
Human Errors ◽  
Important Indicator ◽  
Coal Gas ◽  
Identification Method

The T2statistic is one important indicator of statistical process control theory to identify anomalies of the multivariate industrial process. In the research field of the coal gas pre-drainage process control, previous achievements mainly based on the univariate control chart, which leaded to huge workload and facilitated some human errors. Against these problems, a more comprehensive and easy-to-use method based on the T2statistic was proposed. First at all, the basic thought and the principle of T2control chart was elaborated. Secondly, the data structure and data samples were provided after their principle component analysis. Finally, the multivariate control chart of coal gas pre-drainage process was established. Results show that the proposed anomaly identification method can integrate dozen of univariate control charts into one. Then technicians needn’t deal with many control charts in the same time and many human errors can be avoided.

USING RUN-LENGTH DISTRIBUTIONS OF CONTROL CHARTS TO DETECT FALSE ALARMS

2009 ◽  
Vol 3 (3) ◽  
pp. 217-239 ◽  
Author(s):  
HERBERT MOSKOWITZ ◽  
ROBERT D. PLANTE ◽  
DON G. WARDELL
Keyword(s):  
Control Charts ◽  
False Alarms ◽  
Run Length

scholarly journals Statistical Control Charts: Performances of Short Term Stock Trading in Croatia

2015 ◽  
Vol 6 (1) ◽  
pp. 22-35 ◽  
Author(s):  
Ksenija Dumičić ◽  
Berislav Žmuk
Keyword(s):  
Control Charts ◽  
Stock Exchange ◽  
Moving Average ◽  
Portfolio Analysis ◽  
False Alarms ◽  
Statistical Control ◽  
Stock Trading ◽  
Statistical Tool ◽  
Statistical Process ◽  
Short Run

Abstract Background: The stock exchange, as a regulated financial market, in modern economies reflects their economic development level. The stock market indicates the mood of investors in the development of a country and is an important ingredient for growth. Objectives: This paper aims to introduce an additional statistical tool used to support the decision-making process in stock trading, and it investigate the usage of statistical process control (SPC) methods into the stock trading process. Methods/Approach: The individual (I), exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts were used for gaining trade signals. The open and the average prices of CROBEX10 index stocks on the Zagreb Stock Exchange were used in the analysis. The statistical control charts capabilities for stock trading in the short-run were analysed. Results: The statistical control chart analysis pointed out too many signals to buy or sell stocks. Most of them are considered as false alarms. So, the statistical control charts showed to be not so much useful in stock trading or in a portfolio analysis. Conclusions: The presence of non-normality and autocorellation has great impact on statistical control charts performances. It is assumed that if these two problems are solved, the use of statistical control charts in a portfolio analysis could be greatly improved.

scholarly journals Time Control Chart for Two Inverted Models

2018 ◽  
Vol 7 (3.31) ◽  
pp. 133
Author(s):  
R Subba Rao ◽  
M Pushpa Latha ◽  
R R.L. Kantam
Keyword(s):  
Control Charts ◽  
Life Time ◽  
Quality Characteristic ◽  
Time To Failure ◽  
Time Data ◽  
Statistical Process ◽  
Failure Times ◽  
Process Adjustment ◽  
Time Control ◽  
Control Limits

Control charts are one of the powerful techniques of Statistical Process Control. Control charts are widely accepted and applied in industry which can be used to improve productivity, prevent defects and unnecessary process adjustment. Moreover, they also provide information in diagnosis and process capability. Life time data generally contain the failure times of sample products or inter failure times or number of failures experienced in a given time.  The time to failure of a product is to be considered as a quality characteristic to assess the quality of the product.  Control limits are evaluated for the time to failure.  In this paper the time to failure of a product is considered to follow Inverse Rayleigh and Inverse Half Logistic distributions.  Life time data are compared with the control limits to judge the quality performance of the product.  

A Nonparametric Phase I Control Chart for Monitoring the Process Variability with Individual Observations Based on Empirical Likelihood Ratio

2018 ◽  
Vol 25 (03) ◽  
pp. 1850015 ◽  
Author(s):  
Wei-Heng Huang ◽  
Arthur B. Yeh
Keyword(s):  
Phase I ◽  
Likelihood Ratio ◽  
Control Chart ◽  
Empirical Likelihood ◽  
Control Charts ◽  
Quality Characteristic ◽  
Ratio Test ◽  
Statistical Process ◽  
Empirical Likelihood Ratio ◽  
Empirical Likelihood Ratio Test

Among the statistical process control (SPC) techniques, the control chart has been proven to be effective in process monitoring. The Shewhart chart is one of the most commonly used control charts for monitoring the process mean and variability based on the assumption that the distribution of the quality characteristic is normal. However, in practice, many quality characteristics are not normally distributed. Most of the existing nonparametric control charts are designed for Phase II monitoring. Little has been done in developing the nonparametric Phase I control charts especially for individual observations. In this work, we propose a new nonparametric Phase I control chart for monitoring the scale parameter based on the empirical likelihood ratio test. The simulation results show that the proposed chart is more effective than the existing charts in terms of signal probability. A real example is used to demonstrate how the proposed chart can be applied in practice.

Statistical process control

2015 ◽  
Vol 33 (1) ◽  
pp. 2-24 ◽  
Author(s):  
Soroush Avakh Darestani ◽  
Mina Nasiri
Keyword(s):  
Control Charts ◽  
Fuzzy Numbers ◽  
Fuzzy Theory ◽  
Process Capability ◽  
Quality Characteristic ◽  
Actual Process ◽  
Short Term ◽  
Statistical Process ◽  
Content Type ◽  
Industry Area

Purpose – In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data. However, to measure quality characteristic, practitioners sometimes face with uncertain and linguistic variables. Fuzzy theory is one of the most applicable tools which academia has employed to deal with uncertainty. The paper aims to discuss these issues. Design/methodology/approach – In this investigation, first, fuzzy and S control chart has been developed and second, the fuzzy formulation of the PCIs such as C pm ,C pmu ,C pml , C pmk , P p , P pl , P pu , P pk are constructed when SLs and measurements are at both triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) stages. Findings – The results show that using fuzzy make more flexibility and sense on recognition of out-of-control warnings. Research limitations/implications – For further research, the PCIs for non-normal data can be conducted based on TFN and TrFN. Practical implications – The application case is related to a piston company in Konya’s industry area. Originality/value – In the previous researches, for calculating C p , C pk , C pm and C pmk indices, the base approach was calculate standard deviation for a short term variation. For calculating these indices, the variation between subgroups are being ignored. Therefore, P p and P pk indices solved this fault by mentioning long term and short term variations. Therefore these two indices calculate the actual process capability.

scholarly journals Nonlinear Profile Monitoring Using Spline Functions

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1588
Author(s):  
Hua Xin ◽  
Wan-Ju Hsieh ◽  
Yuhlong Lio ◽  
Tzong-Ru Tsai
Keyword(s):  
Control Charts ◽  
Integrated Control ◽  
False Alarms ◽  
Model Parameters ◽  
Mathematical Form ◽  
Profile Model ◽  
Normality Assumption ◽  
Vertical Density ◽  
Error Terms

In this study, two new integrated control charts, named T2-MAE chart and MS-MAE chart, are introduced for monitoring the quality of a process when the mathematical form of nonlinear profile model for quality measure is complicated and unable to be specified. The T2-MAE chart is composed of two memoryless-type control charts and the MS-MAE chart is composed of one memory-type and one memoryless-type control charts. The normality assumption of error terms in the nonlinear profile model for both proposed control charts are extended to a generalized model. An intensive simulation study is conducted to evaluate the performance of the T2-MAE and MS-MAE charts. Simulation results show that the MS-MAE chart outperforms the T2-MAE chart with less false alarms during the Phase I monitoring. Moreover, the MS-MAE chart is sensitive to different shifts on the model parameters and profile shape during the Phase II monitoring. An example about the vertical density profile is used for illustration.

APPLICATION OF SPC AND GROWTH MODELS ON THE ECONOMIC TRAJECTORY OF THE LEATHER AND FOOTWEAR INDUSTRY

2020 ◽  
Author(s):  
Mario Lesina ◽  
Lovorka Gotal Dmitrovic
Keyword(s):  
Human Capital ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Growth Models ◽  
Pearson Correlation ◽  
Small Companies ◽  
Statistical Process ◽  
Production Staff ◽  
Footwear Industry

The paper shows the relation among the number of small, medium and large companies in the leather and footwear industry in Croatia, as well as the relation among the number of their employees by means of the Spearman and Pearson correlation coefficient. The data were collected during 21 years. The warning zone and the risk zone were determined by means of the Statistical Process Control (SPC) for a certain number of small, medium and large companies in the leather and footwear industry in Croatia. Growth models, based on externalities, models based on research and development and the AK models were applied for the analysis of the obtained research results. The paper shows using the correlation coefficients that The relation between the number of large companies and their number of employees is the strongest, i.e. large companies have the best structured work places. The relation between the number of medium companies and the number of their employees is a bit weaker, while there is no relation in small companies. This is best described by growth models based on externalities, in which growth generates the increase in human capital, i.e. the growth of the level of knowledge and skills in the entire economy, but also deductively in companies on microeconomic level. These models also recognize the limit of accumulated knowledge after which growth may be expected. The absence of growth in small companies results from an insufficient level of human capital and failure to reach its limit level which could generate growth. According to Statistical Process Control (SPC), control charts, as well as regression models, it is clear that the most cost-effective investment is the investment into medium companies. The paper demonstrates the disadvantages in small, medium and large companies in the leather and footwear industry in Croatia. Small companies often emerge too quickly and disappear too easily owing to the employment of administrative staff instead of professional production staff. As the models emphasize, companies need to invest into their employees and employ good production staff. Investment and support to the medium companies not only strengthens the companies which have a well-arranged technological process and a good systematization of work places, but this also helps large companies, as there is a strong correlation between the number of medium and large companies.

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