Linear profile monitoring using an adaptive EWMA control chart

2010 ◽  
Author(s):  
Giovanna Capizzi ◽  
Guido Masarotto
Keyword(s):  
Control Chart ◽  
Profile Monitoring ◽  
Linear Profile ◽  
Ewma Control Chart

scholarly journals Monitoring process mean with a new EWMA control chart

Production ◽  
2011 ◽  
Vol 21 (2) ◽  
pp. 217-222 ◽  
Author(s):  
Yang Su-Fen ◽  
Tsai Wen-Chi ◽  
Huang Tzee-Ming ◽  
Yang Chi-Chin ◽  
Cheng Smiley
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
Population Distribution ◽  
Average Run Length ◽  
Process Data ◽  
Ewma Chart ◽  
Ewma Control Chart ◽  
Simple Statistic ◽  
Run Lengths ◽  
Proper Values

In practice, sometimes the process data did not come from a known population distribution. So the commonly used Shewhart variables control charts are not suitable since their performance could not be properly evaluated. In this paper, we propose a new EWMA Control Chart based on a simple statistic to monitor the small mean shifts in the process with non-normal or unknown distributions. The sampling properties of the new monitoring statistic are explored and the average run lengths of the proposed chart are examined. Furthermore, an Arcsine EWMA Chart is proposed since the average run lengths of the Arcsine EWMA Chart are more reasonable than those of the new EWMA Chart. The Arcsine EWMA Chart is recommended if we are concerned with the proper values of the average run length.

Body Welding Quality Control Based on a CUSUM Control Chart

2011 ◽  
Vol 228-229 ◽  
pp. 1080-1084
Author(s):  
Rong Li ◽  
Jing Li ◽  
Jian Liu
Keyword(s):  
Quality Control ◽  
Sample Size ◽  
Control Chart ◽  
Quality Inspection ◽  
Welding Quality ◽  
Ewma Control Chart ◽  
Cusum Control Chart ◽  
Abnormal Variation ◽  
Quality Variation

Aiming at the situation in some Chinese auto companies that the workload of body welding quality inspection is high and the sample size is extremely small, a brand-new CUSUM Control Chart for variance monitoring is proposed in the paper to realize the effective quality control in body welding variance, whose principle is to use variance statistics based on Queensberry transformation Φ-1(G((n-1) St2/σ02)) to monitor infinitely small variances in the process of body welding. Evaluation instance results show that, compared with traditional CUSUM control chart, EWMA control chart and weighted CUSUM control chart, the proposed CUSUM control chart based on variance monitoring is more sensitive to the abnormal variation fluctuation and can detect the abnormity of quality variation earlier.

A EWMA Control Chart for Exponential Distributed Quality Based on Moving Average Statistics

2015 ◽  
Vol 32 (3) ◽  
pp. 1179-1190 ◽  
Author(s):  
Nasrullah Khan ◽  
Muhammad Aslam ◽  
Chi-Hyuck Jun
Keyword(s):  
Control Chart ◽  
Moving Average ◽  
Ewma Control Chart

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.

An efficient adaptive EWMA control chart for monitoring the process mean

2018 ◽  
Vol 34 (4) ◽  
pp. 563-571 ◽  
Author(s):  
Abdul Haq ◽  
Rabia Gulzar ◽  
Michael B. C. Khoo
Keyword(s):  
Control Chart ◽  
Process Mean ◽  
Ewma Control Chart

A novel single composite Shewhart‐EWMA control chart for monitoring the process mean

2021 ◽  
Author(s):  
Jean‐Claude Malela‐Majika ◽  
Sandile C. Shongwe ◽  
Philippe Castagliola ◽  
Ruffin M. Mutambayi
Keyword(s):  
Control Chart ◽  
Process Mean ◽  
Ewma Control Chart
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