On the design of Shewhart control charts for count time series under estimation uncertainty

2021 ◽  
Vol 157 ◽  
pp. 107331
Author(s):  
Christian H. Weiß ◽  
Murat Caner Testik ◽  
Annika Homburg
Keyword(s):  
Time Series ◽  
Control Charts ◽  
Count Time Series ◽  
Shewhart Control Charts ◽  
Estimation Uncertainty ◽  
Shewhart Control

PERFORMANCE OF SENSITIZING RULES ON SHEWHART CONTROL CHARTS WITH AUTOCORRELATED DATA

2001 ◽  
Vol 08 (02) ◽  
pp. 159-171 ◽  
Author(s):  
SANDY D. BALKIN ◽  
DENNIS K. J. LIN
Keyword(s):  
Time Series ◽  
Control Charts ◽  
Moving Average ◽  
Correlated Data ◽  
Autoregressive Moving Average ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
The Mean ◽  
Autocorrelated Data ◽  
Using Data

Sensitizing Rules are commonly applied to Shewhart Charts to increase their effectiveness in detecting shifts in the mean that may otherwise go unnoticed by the usual "out-of-control" signals. The purpose of this paper is to demonstrate how well these rules actually perform when the data exhibit autocorrelation compared to non-correlated data. Since most control chart data are collected as time series, it is of interest to examine the performance of Shewhart's [Formula: see text] Chart using data generated from typical time series models. In this paper, measurements arising from autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA) processes are examined using Shewhart Control Charts in conjunction with several sensitizing rules. The results indicate that the rules work well when there are strong autocorrelative relationships, but are not as effective in recognizing small to moderate levels of correlation. We conclude with the recommendation to practitioners that they use a more definitive measure of autocorrelation such as the Sample Autocorrelation Function correlogram to detect dependency.

Shewhart Control Charts for Monitoring Reliability with Weibull Lifetimes

2014 ◽  
Vol 31 (8) ◽  
pp. 1565-1574 ◽  
Author(s):  
Alireza Faraz ◽  
Erwin M. Saniga ◽  
Cedric Heuchenne
Keyword(s):  
Control Charts ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Monitoring Reliability

scholarly journals One-sided Shewhart control charts for monitoring the ratio of two normal variables in short production runs

2021 ◽  
Vol 69 ◽  
pp. 273-289
Author(s):  
Kim Duc Tran ◽  
Qurat-Ul-Ain Khaliq ◽  
Adel Ahmadi Nadi ◽  
Thi Hien Nguyen ◽  
Kim Phuc Tran
Keyword(s):  
Control Charts ◽  
Shewhart Control Charts ◽  
Shewhart Control

scholarly journals Shewhart's control charts and process capability ratio applied to a sewage treatment station

2014 ◽  
Vol 34 (4) ◽  
pp. 770-779 ◽  
Author(s):  
Fábio Orssatto ◽  
Marcio A. Vilas Boas ◽  
Ricardo Nagamine ◽  
Miguel A. Uribe-Opazo
Keyword(s):  
Control Charts ◽  
Suspended Solids ◽  
Sewage Treatment ◽  
Oxygen Demand ◽  
Process Capability ◽  
Total Suspended Solids ◽  
Statistical Control ◽  
Environmental Legislation ◽  
Shewhart Control Charts ◽  
Shewhart Control

The current study used statistical methods of quality control to evaluate the performance of a sewage treatment station. The concerned station is located in Cascavel city, Paraná State. The evaluated parameters were hydrogenionic potential, settleable solids, total suspended solids, chemical oxygen demand and biochemical oxygen demand in five days. Statistical analysis was performed through Shewhart control charts and process capability ratio. According to Shewhart charts, only the BOD(5.20) variable was under statistical control. Through capability ratios, we observed that except for pH the sewage treatment station is not capable to produce effluents under characteristics that fulfill specifications or standard launching required by environmental legislation.

scholarly journals A Fuzzy Control Chart Approach for Attributes and Variables

2018 ◽  
Vol 8 (5) ◽  
pp. 3360-3365 ◽  
Author(s):  
N. Pekin Alakoc ◽  
A. Apaydin
Keyword(s):  
Quality Control ◽  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Fuzzy Theory ◽  
New Approach ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Quality Control Chart

The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach has better performance and can detect the process shifts efficiently.

A control chart for severity index to detect drought compares favourably to logistic regression

2011 ◽  
Vol 42 (1) ◽  
pp. 1-9
Author(s):  
Jared Townsley ◽  
Justin R Chimka
Keyword(s):  
Logistic Regression ◽  
Control Chart ◽  
Control Charts ◽  
Severity Index ◽  
Drought Severity ◽  
Prediction Errors ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Accessible Method ◽  
Made In

We describe the discovery of how a traditional control chart for the Palmer Drought Severity Index (PDSI) to detect drought compares favourably to a theoretically appropriate statistical (logistic regression) model of drought as a function of PDSI. Our empirical results are based on monthly observations of PDSI, precipitation and temperature made in Kansas since 1895. Results from the study suggest that a relatively simple statistical approach based on Shewhart control charts may provide a more accessible method for relevant government agencies to predict droughts, improving resource management and preparation. Moreover, utilizing such an approach over more sophisticated methods may come at little expense regarding prediction errors.

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