论文信息 - Robustness of Forecast-Based Monitoring Schemes

Robustness of Forecast-Based Monitoring Schemes

Forecast-based monitoring schemes for monitoring autocorrelated data are two stages processes. The first step is to determine an appropriate time-series model for the process data. The process is then monitored using control charts applied to the one-st..

Benjamin M. Adams | Iou-Tsyr Tseng

[1] Fah Fatt Gan,et al. An optimal design of CUSUM quality control charts , 1991 .

[2] Douglas C. Montgomery,et al. Some Statistical Process Control Methods for Autocorrelated Data , 1991 .

[3] E. S. Page. CONTINUOUS INSPECTION SCHEMES , 1954 .

[4] W. J. Padgett,et al. On the α-risks for shewhart control charts , 1992 .

[5] T. Harris,et al. Statistical process control procedures for correlated observations , 1991 .

[6] Herbert Moskowitz,et al. Run-Length Distributions of Special-Cause Control Charts for Correlated Processes , 1994 .

[7] Donald J. Wheeler,et al. Detecting a Shift in Process Average: Tables of the Power Function for X Charts , 1983 .

[8] Benjamin M. Adams,et al. Monitoring autocorrelated processes with an exponentially weighted moving average forecast , 1994 .

[9] Richard A. Johnson,et al. The Effect of Serial Correlation on the Performance of CUSUM Tests II , 1974 .

[10] Benjamin M. Adams,et al. Combined Control Charts for Forecast-Based Monitoring Schemes , 1996 .

[11] E. S. Page. CONTROL CHARTS WITH WARNING LINES , 1955 .

[12] William H. Woodall,et al. The effect of autocorrelation on the retrospective X-chart , 1992 .

[13] Stephen V. Crowder,et al. Design of Exponentially Weighted Moving Average Schemes , 1989 .

[14] William H. Woodall,et al. Performance of the Control Chart Trend Rule under Linear Shift , 1988 .