An $(\tilde{X}/R)$-EWMA CONTROL CHART FOR MONITORING THE PROCESS SAMPLE MEDIAN

2001 ◽  
Vol 08 (02) ◽  
pp. 123-135 ◽  
Author(s):  
PHILIPPE CASTAGLIOLA
Keyword(s):  
Control Chart ◽  
Optimal Parameters ◽  
Sample Median ◽  
Ewma Control Chart ◽  
Control Limits

The method proposed in this paper is a new EWMA type control chart, dedicated to the monitoring of the process sample median [Formula: see text]. Because this control chart uses the range of the process, we call it a [Formula: see text]-EWMA control chart. In this paper, we show how to compute the control limits of this chart, give an illustrative example, describe how to compute the ARL and how to obtain optimal parameters minimizing the out-of-control ARL.

scholarly journals Application of Residual-Based EWMA Control Charts for Detecting Faults in Variable-Air-Volume Air Handling Unit System

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Haitao Wang
Keyword(s):  
Time Series ◽  
Fault Detection ◽  
Control Chart ◽  
Detection Method ◽  
Time Data ◽  
Air Handling Unit ◽  
Ewma Control Chart ◽  
Robust Fault Detection ◽  
Modeling Errors ◽  
Control Limits

An online robust fault detection method is presented in this paper for VAV air handling unit and its implementation. Residual-based EWMA control chart is used to monitor the control processes of air handling unit and detect faults of air handling unit. In order to provide a level of robustness with respect to modeling errors, control limits are determined by incorporating time series model uncertainty in EWMA control chart. The fault detection method proposed was tested and validated using real time data collected from real VAV air-conditioning systems involving multiple artificial faults. The results of validation show residual-based EWMA control chart with designing control limits can improve the accuracy of fault detection through eliminating the negative effects of dynamic characteristics, serial correlation, normal transient changes of system, and time series modeling errors. The robust fault detection method proposed can provide an effective tool for detecting the faults of air handling units.

A R-EWMA CONTROL CHART FOR MONITORING THE PROCESS RANGE

2005 ◽  
Vol 12 (01) ◽  
pp. 31-49 ◽  
Author(s):  
PHILIPPE CASTAGLIOLA
Keyword(s):  
Optimal Design ◽  
Control Chart ◽  
Design Strategy ◽  
Logarithmic Transformation ◽  
Ewma Control Chart ◽  
Control Limits

This article demonstrates how a three parameter logarithmic transformation combined with an EWMA approach can be used to monitor the range of a process. The computation of the parameters of the logarithmic transformation and the control limits is explained. An easy-to-use table is provided, and illustrative examples are given. The performance of the logarithmic transformation is evaluated under the assumption of normality. An optimal design strategy based on the ARL is presented, and comparisons with other procedures are performed.

Evaluation of an np control chart under truncated life test using quick switching sampling system

2017 ◽  
Vol 40 (12) ◽  
pp. 3407-3414 ◽  
Author(s):  
P Jeyadurga ◽  
S Balamurali
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Optimal Parameters ◽  
Life Test ◽  
Sampling System ◽  
Run Length ◽  
Truncated Life Test ◽  
Control Limits

In this paper, we propose a new attribute np control chart for monitoring Weibull distributed mean life of a product using a quick switching sampling system. The proposed control chart consists of two pairs of control limits, namely normal and tightened control limits. The optimal parameters of the proposed control chart, such as coefficients of control limits and experiment termination ratio, are determined so that the average run length (ARL) is close to the target in-control ARL. The ARL is calculated for various shift constants for the corresponding determined parameters. The performance of the proposed control chart is evaluated and compared with other existing charts in terms of ARL.

scholarly journals Penerapan Peta Kendali Neutrosophic Exponentially Weighted Moving Average (NEWMA) X dalam Monitoring Rata-Rata Proses Ketebalan Kaca

2022 ◽  
Vol 4 (1) ◽  
Author(s):  
Wibawati Wibawati ◽  
Widya Amalia Rahma ◽  
Muhammad Ahsan ◽  
Wilda Melia Udiatami
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Quality Characteristic ◽  
Average Process ◽  
Exponentially Weighted Moving Average ◽  
Ewma Control Chart ◽  
Control Limits ◽  
Weighted Moving Average ◽  
Exponentially Weighted

In the industrial sector, the measurement results of a quality characteristic often involve an uncertainty interval (interval indeterminacy). This causes the classical control chart to be less suitable for monitoring quality. Currently, a control chart with a neutrosophic approach has been developed. The neutrosophic control chart was developed based on the concept of neutrosophic numbers with control charts. One of the control charts that have been developed to monitor the mean process is the Neutrosophic Exponentially Weighted Moving Average (NEWMA) X control chart. This control chart is a combination of neutrosophic with classical EWMA control chart.  The neutrosophic control chart consists of two control charts, namely lower and upper, each of which consists of upper and lower control limits. Therefore, NEWMA X is more sensitive to detect out-of-control observations. In this research, the NEWMA X control chart will be used to monitor the average process of the thickness of the panasap dark grey 5mm glass produced by a glass industry. Through the analysis in this research, it was found that by using weighting λN [0, 10; 0, 10] and constant value kN [2, 565; 2, 675], the average process of the thickness of panasap dark grey 5mm glass has not beet controlled statistically because 21 observations were identified that were outside the control limits (out of control). When compared with the classical EWMA control chart with the same weighting λ, 17 observations were detected out of control. This proves that the NEWMA X control chart is more sensitive in detecting observations that are out of control because the determination of the in-control state is based on two values, lower and upper, both at the lower and upper control limits.

scholarly journals Average Run Length on CUSUM Control Chart for Seasonal and Non-Seasonal Moving Average Processes with Exogenous Variables

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 173
Author(s):  
Rapin Sunthornwat ◽  
Yupaporn Areepong
Keyword(s):  
Control Chart ◽  
Average Run Length ◽  
Moving Average ◽  
Optimal Parameters ◽  
Exogenous Variables ◽  
Explicit Formulas ◽  
Run Length ◽  
Ewma Control Chart ◽  
Cusum Control Chart ◽  
Moving Average Processes

The aim of this study was to derive explicit formulas of the average run length (ARL) of a cumulative sum (CUSUM) control chart for seasonal and non-seasonal moving average processes with exogenous variables, and then evaluate it against the numerical integral equation (NIE) method. Both methods had similarly excellent agreement, with an absolute percentage error of less than 0.50%. When compared to other methods, the explicit formula method is extremely useful for finding optimal parameters when other methods cannot. In this work, the procedure for obtaining optimal parameters—which are the reference value ( a ) and control limit ( h )—for designing a CUSUM chart with a minimum out-of-control ARL is presented. In addition, the explicit formulas for the CUSUM control chart were applied with the practical data of a stock price from the stock exchange of Thailand, and the resulting performance efficiency is compared with an exponentially weighted moving average (EWMA) control chart. This comparison showed that the CUSUM control chart efficiently detected a small shift size in the process, whereas the EWMA control chart was more efficient for moderate to large shift sizes.

scholarly journals On Performance of Two-Parameter Gompertz-Based X¯ Control Charts

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Johnson A. Adewara ◽  
Kayode S. Adekeye ◽  
Olubisi L. Aako
Keyword(s):  
Control Chart ◽  
Coverage Probability ◽  
Control Charts ◽  
Average Run Length ◽  
Real Life ◽  
Correction Method ◽  
Gompertz Distribution ◽  
Real Life Data ◽  
Control Limits ◽  
Two Parameter

In this paper, two methods of control chart were proposed to monitor the process based on the two-parameter Gompertz distribution. The proposed methods are the Gompertz Shewhart approach and Gompertz skewness correction method. A simulation study was conducted to compare the performance of the proposed chart with that of the skewness correction approach for various sample sizes. Furthermore, real-life data on thickness of paint on refrigerators which are nonnormal data that have attributes of a Gompertz distribution were used to illustrate the proposed control chart. The coverage probability (CP), control limit interval (CLI), and average run length (ARL) were used to measure the performance of the two methods. It was found that the Gompertz exact method where the control limits are calculated through the percentiles of the underline distribution has the highest coverage probability, while the Gompertz Shewhart approach and Gompertz skewness correction method have the least CLI and ARL. Hence, the two-parameter Gompertz-based methods would detect out-of-control faster for Gompertz-based X¯ charts.

Sign in / Sign up

Export Citation Format

Share Document