Memory type control charts with inverse-Gaussian response: An application to yarn manufacturing industry

2020 ◽  
pp. 014233122095296
Author(s):  
Muhammad Amin ◽  
Tahir Mahmood ◽  
Summera Kinat
Keyword(s):  
Simulation Study ◽  
Control Charts ◽  
Manufacturing Industry ◽  
Moving Average ◽  
Inverse Gaussian ◽  
Monitoring Methods ◽  
Pearson Residuals ◽  
Memory Type ◽  
Exponential Weighted Moving Average ◽  
The Relationship

Control charts are commonly applied for monitoring and controlling the performance of the manufacturing process. Usually, control charts are designed based on the main quality characteristics variable. However, there exist numerous other variables which are highly associated with the main variable. Therefore, generalized linear model (GLM)-based control charts were used, which are capable of maintaining the relationship between variables and of monitoring an abrupt change in the process mean. This study is an effort to develop the Phase II GLM-based memory type control charts using the deviance residuals (DR) and Pearson residuals (PR) of inverse Gaussian (IG) regression model. For evaluation, a simulation study is designed, and the performance of the proposed control charts is compared with the counterpart memory less control charts and data-based control charts (excluding the effect of covariate) in terms of the run length properties. Based on the simulation study, it is concluded that the exponential weighted moving average (EWMA) type control charts have better detection ability as compared with Shewhart and cumulative sum (CUSUM) type control charts under the small or/and moderate shift sizes. Moreover, it is shown that utilizing covariate may lead to useful conclusions. Finally, the proposed monitoring methods is implemented on the dataset related to the yarn manufacturing industry to highlight the importance of the proposed control chart.

scholarly journals Memory Type Ratio and Product Estimators in Stratified Sampling

2020 ◽  
Author(s):  
Irfan Aslam ◽  
Muhammad Noor-ul-Amin ◽  
Uzma Yasmeen ◽  
Muhammad Hanif
Keyword(s):  
Simulation Study ◽  
Moving Average ◽  
Real Life ◽  
Auxiliary Information ◽  
Stratified Sampling ◽  
Real Life Data ◽  
Memory Type ◽  
Exponential Weighted Moving Average ◽  
Mean Square Errors ◽  
Weighted Moving Average

The exponential weighted moving average (EWMA) statistic is utilized the past information along with the present to enhance the efficiency of the estimators of the population parameters. In this study, the EWMA statistic is used to estimate the population mean with auxiliary information. The memory type ratio and product estimators are proposed under stratified sampling (StS). Mean square errors (MSE) expressions and relative efficiencies of the proposed estimators are derived. An extensive simulation study is conducted to evaluate the performance of the proposed estimators. An empirical study is presented based on real-life data that supports the findings of the simulation study.

scholarly journals Nonparametric EWMA-Type Control Charts for Monitoring Industrial Processes: An Overview

2021 ◽  
Vol 6 (3) ◽  
pp. 708-751
Author(s):  
Ioannis S. Triantafyllou ◽  
Mangey Ram
Keyword(s):  
Probability Distribution ◽  
Statistical Process Control ◽  
Crucial Role ◽  
Control Charts ◽  
Moving Average ◽  
Industrial Processes ◽  
Statistical Process ◽  
Practical Applications ◽  
Memory Type ◽  
Weighted Moving Average

In the present paper we provide an up-to-date overview of nonparametric Exponentially Weighted Moving Average (EWMA) control charts. Due to their nonparametric nature, such memory-type schemes are proved to be very useful for monitoring industrial processes, where the output cannot match to a particular probability distribution. Several fundamental contributions on the topic are mentioned, while recent advances are also presented in some detail. In addition, some practical applications of the nonparametric EWMA-type control charts are highlighted, in order to emphasize their crucial role in the contemporary online statistical process control.

scholarly journals ON THE HOTELLING’S T, MCUSUM AND MEWMA CONTROL CHARTS' PERFORMANCE WITH DIFFERENT VARISBILITY SOURCES: A SIMULATION STUDY

2015 ◽  
Vol 12 (2) ◽  
pp. 196 ◽  
Author(s):  
D. A. O. Moraes ◽  
F. L. P. Oliveira ◽  
L. H. Duczmal
Keyword(s):  
Simulation Study ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Control Process ◽  
Average Time To Signal ◽  
Mean Vectors ◽  
Multivariate Control ◽  
The Impact ◽  
Mean Vector

This work is a simulation study to investigate the sensitivity of multivariate control charts for monitoring mean vectors in a bivariate Gaussian process with individual observations. The multivariate cumulative sum (MCUSUM), the multivariate exponentially weighted moving average (MEWMA) and Hotelling’s T charts are selected for analysis due to their common dependency on the noncentrality parameter. The chart performance is evaluated through the average run length (ARL) or the average time to signal. The impact of utilising in-control limits computed from known parameters or Phase I sample estimates is considered for mean vector shifts. Although designed to monitor mean vectors, the sensibility of the control charts is additionally analysed through different variability sources, including the mixing effect of mean vector shifts with increasing variances or positive autocorrelation in the out-of-control process. 

scholarly journals Capabilities of Statistical Residual-Based Control Charts in Short- and Long-Term Stock Trading

2016 ◽  
Vol 62 (1) ◽  
pp. 12-26 ◽  
Author(s):  
Berislav Žmuk
Keyword(s):  
Control Chart ◽  
Stock Prices ◽  
Control Charts ◽  
Stock Exchange ◽  
Moving Average ◽  
Stock Trading ◽  
Cusum Control Chart ◽  
Long Run ◽  
Short Run ◽  
Exponential Weighted Moving Average

Abstract The aim of this paper is to introduce and develop additional statistical tools to support the decision-making process in stock trading. The prices of CROBEX10 index stocks on the Zagreb Stock Exchange were used in the paper. The conducted trading simulations, based on the residual-based control charts, led to an investor’s profit in 67.92% cases. In the short run, the residual-based cumulative sum (CUSUM) control chart led to the highest portfolio profits. In the long run, when average stock prices were used and 2-sigma control limits set, the residual-based exponential weighted moving average control chart had the highest portfolio profit. In all other cases in the long run, the CUSUM control chart appeared to be the best choice. The acknowledgment that the SPC methods can be successfully used in stock trading will, hopefully, increase their use in this field.

scholarly journals Profile Monitoring for Autocorrelated Reflow Processes with Small Samples

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 104 ◽  
Author(s):  
Shu-Kai Fan ◽  
Chih-Hung Jen ◽  
Jai-Xhing Lee
Keyword(s):  
Control Charts ◽  
Moving Average ◽  
Model Fitting ◽  
Process Models ◽  
Small Samples ◽  
Parameter Estimates ◽  
Monitoring Methods ◽  
Profile Monitoring ◽  
Statistical Process ◽  
Monitoring Method

The methodology of profile monitoring combines both the model fitting and statistical process control (SPC) techniques. Over the past ten years, a variety of profile monitoring methods have been proposed and extensively investigated in terms of different process profiles. However, monitoring tasks still exhibit a primary problem in that the errors surrounding the functional relationship are frequently assumed to be independent within every single profile. However, the assumption of independence is an unrealistic assumption in many practical instances. In particular, within-profile autocorrelation often occurs in the profile data. To mitigate the within-profile autocorrelation, a monitoring method incorporating an autoregressive (AR)(1) model to cope with autocorrelation is proposed. In this paper, the reflow process with small samples in surface mount technology (SMT) is investigated. In Phase I, three different process models are compared in combination with the first-order autoregressive model, while an appropriate profile model is sought. The Hotelling T2 and exponentially weighted moving average (EWMA) control charts are used together to monitor the parameter estimates (i.e., profile shape) and residuals (i.e., profile variability), respectively.

scholarly journals Moving Average control charts for Burr X and Inverse Gaussian distributions

2020 ◽  
Vol 30 (4) ◽  
Author(s):  
Ambreen Shafqat ◽  
Muhammad Aslam ◽  
Mohammed Albassam
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Moving Average ◽  
Inverse Gaussian ◽  
Life Test ◽  
Run Length ◽  
Truncated Life Test ◽  
Average Control ◽  
Better Than

The Burr X and Inverse Gaussian (IG) distributions are considered in this paper to design an attribute control chart for time truncated life test with Moving Average (MA) scheme w. The presentation of the MA control chart is estimated in terms of average run length (ARL) by using the Monte Carlo simulation. The ARL is decided for different values of sample sizes, MA statistics size, parameters’ values, and specified average run length. The performance of this new MA attribute control chart is compared with the usual time truncated control chart for Burr X and IG distributions. The performance of a new control chart is better than the existing control chart.

scholarly journals The Performance of Robust Multivariate Ewma Control Charts

2018 ◽  
pp. 52-58
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Statistical Tool ◽  
M Estimator ◽  
One Step ◽  
Exponential Weighted Moving Average ◽  
Mewma Control Chart ◽  
Mean Vector ◽  
Weighted Moving Average

Multivariate Exponential Weighted Moving Average (MEWMA) control chart is a popular statistical tool for monitoring multivariate process over time. However, this chart is sensitive to the presence of outliers arising from the use of classical mean vector and covariance matrix in estimating the MEWMA statistic. These classical estimators are known to be sensitive to the outliers. To address this problem, robust MEWMA control charts based on modified one-step M-estimator (MOM) and Winsorized modified one-step M-estimator (WM) are proposed. Their performance is then compared with the standard MEWMA control chart in various situations. The findings revealed that the proposed robust MEWMA control charts are more effective in controlling false alarm rates especially for large sample sizes and high percentage of outliers.

scholarly journals Novel Condition Monitoring Method for Wind Turbines Based on the Adaptive Multivariate Control Charts and SCADA Data

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Qinkai Han ◽  
Zhentang Wang ◽  
Tao Hu
Keyword(s):  
Control Chart ◽  
Condition Monitoring ◽  
Control Charts ◽  
Moving Average ◽  
Monitoring Method ◽  
Abnormal State ◽  
Exponential Weighted Moving Average ◽  
Multivariate Control ◽  
Mewma Control Chart ◽  
Weighted Moving Average

A novel condition monitoring method based on the adaptive multivariate control charts and the supervisory control and data acquisition (SCADA) system is developed. Two types of control charts are adopted: one is the adaptive exponential weighted moving average (AEWMA) control chart for abnormal state detection, and the other is the multivariate exponential weighted moving average (MEWMA) control chart for anomaly location determination. Optimization procedures for these control charts are implemented to achieve minimum out-of-control average running length. Multivariate regression analysis is utilized to obtain the normal condition prediction model of wind turbine with fault-free SCADA data. After comparing the regression accuracy of several popular algorithms in the MRA, the random forest is adopted for feature selection and regression prediction. Various tests on the wind turbine with normal and abnormal states are conducted. The performance and robustness of various control charts are compared comprehensively. Compared with conventional control charts, the AEWMA control chart is more sensitive to the abnormal state and thus has a more effective anomaly identification ability and better robustness. It is shown that the MEWMA control chart combined with the out-of-limit number index can effectively locate and identify the abnormal component.

Using The Fully Modified M Method In Estimating The Effect Of Cultivated Area On Barley Production In Iraq For The Period 1970-2018

2020 ◽  
Vol 2020 (66) ◽  
pp. 101-110
Author(s):  
. Azhar Kadhim Jbarah ◽  
Prof Dr. Ahmed Shaker Mohammed
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Estimation Method ◽  
Estimation Methods ◽  
Autoregressive Moving Average ◽  
Moving Average Models ◽  
Error Terms ◽  
Relationship Of ◽  
The Relationship ◽  
Barley Crop

The research is concerned with estimating the effect of the cultivated area of barley crop on the production of that crop by estimating the regression model representing the relationship of these two variables. The results of the tests indicated that the time series of the response variable values is stationary and the series of values of the explanatory variable were nonstationary and that they were integrated of order one ( I(1) ), these tests also indicate that the random error terms are auto correlated and can be modeled according to the mixed autoregressive-moving average models ARMA(p,q), for these results we cannot use the classical estimation method to estimate our regression model, therefore, a fully modified M method was adopted, which is a robust estimation methods, The estimated results indicate a positive significant relation between the production of barley crop and cultivated area.

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