Name
Title | ||
|---|---|---|
Exponentially weighted moving average chart with a likelihood ratio test for monitoring autocorrelated processes |
Abstract | ||
|---|---|---|
In this article, an exponential weighted moving average chart based on a likelihood ratio test is developed to monitor the mean and variance shifts simultaneously for autocorrelated processes. A simple method is used to transform the positively autocorrelated data to the negatively autocorrelated data. The average run length of the proposed chart is derived from a simulation approach. The performance of our proposed chart is compared with some existing charts. The results show that the proposed chart provides better performance for detecting a wide range of shifts in the process mean and variance simultaneously. Additionally, the economic performance of different charts under the first-order autoregressive model is provided. A real example of a stepper motor in the heating, ventilation, and air conditioning module is used to demonstrate the application of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1002/qre.2602 | QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL |
Keywords | Field | DocType |
autocorrelated process,EWMA,likelihood ratio test,simulation approach | Econometrics,Likelihood-ratio test,Exponentially weighted moving average,EWMA chart,Chart,Engineering,Statistics,Autocorrelation | Journal |
Volume | Issue | ISSN |
36.0 | 2.0 | 0748-8017 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fu-Kwun Wang | 1 | 94 | 22.41 |
| Xiao‐Bin Cheng | 2 | 0 | 0.34 |