Name
Abstract | ||
|---|---|---|
The usual key assumptions in designing quality control charts are the normality and independency of serial samples. While the normality assumption holds in most cases, in many continuous-flow processes such as the chemical processes, serial samples have some degrees of autocorrelation associated with them. Ignoring the autocorrelation structure in constructing control charts, results in decreasing the in-control run length, and so increasing the false alarms. Moreover, when the object is to detect small shifts in the mean vector of a process, the performance of Cumulative Sum (CUSUM) control charts is dramatically better than Schewhart control charts. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.amc.2006.12.081 | Applied Mathematics and Computation |
Keywords | Field | DocType |
MCUSUM control charts,Autocorrelation,Residual chart,Average run length,Artificial neural networks,Multi-layer perceptrons | Normality,Autoregressive model,CUSUM,Computer science,Multivariate statistics,Control chart,Chart,Statistics,Artificial neural network,Autocorrelation | Journal |
Volume | Issue | ISSN |
189 | 2 | 0096-3003 |
Citations | PageRank | References |
6 | 0.72 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jamal Arkat | 1 | 80 | 7.19 |
| Seyed Taghi Akhavan Niaki | 2 | 624 | 57.47 |
| B. Abbasi | 3 | 145 | 19.89 |