Advanced SearchSearch Tips
A CUSUM Chart for Detecting Mean Shifts of Oscillating Pattern
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
A CUSUM Chart for Detecting Mean Shifts of Oscillating Pattern
Lee, Jae-June; Kim, Duk-Rae; Lee, Jong-Seon;
  PDF(new window)
The cumulative sum(CUSUM) control charts are typically used for detecting small level shifts in process control. To control an auto-correlated process, the model-based control methods can be employed, in which the residuals from fitting a time series model are applied to the CUSUM chart. However, the persistent level shifts in the original process may lead to varying mean shifts in residuals, which may deteriorate detection performance significantly. Therefore, in this paper, focussing on ARMA(1,1), we propose a new CUSUM type control method which can detect the dynamic mean shifts in residuals especially with oscillating pattern effectively and, through the simulation study, evaluate its performance by comparing with other various CUSUM type control methods introduced so far.
CUSUM;WCUSUM;auto-correlated process control;dynamic shifts;
 Cited by
Neuro Fuzzy와 WECR 기법에 의한 상수관망 누수예측,황재문;이호현;신강욱;김남;

한국지능시스템학회논문지, 2017. vol.27. 4, pp.349-356 crossref(new window)
이재준, 이종선 (2008). 자기상관 공정에 대한 누적합 관리도에서 설계모수값의 결정, <품질경영학회지>, 6, 21–37

Alwan, L. C. and Roberts, H. V. (1988). Time series modeling for statistical process control Journal of Business & Economic Statistics, 6, 87–95

Atienza, O. O., Tang, L. C. and Ang, B. W. (1998). A SPC procedure for detecting level shifts of autocorrelated processes, Journal of Quality Technology, 30, 340–351

Brook, D. and Evans, D. (1972). An approach to the probability distribution of cusum run length, Biometrika, 59, 539–549 crossref(new window)

Han, D., Tsung, F., Hu, X. and Wang, K. (2007). CUSUM and EWMA multi-charts for detecting a range of mean shifts, Statistica Sinica, 17, 1139–1164

Hawkins, D. M. and Olwell, D. H. (1998).Cumulative Sum Charts and Charting for Quality Improvement, Springer-Verlag, New York

Hu, S. J. and Roan, C. (1996). Change patterns in the time series-based control charts, Journal of Quality Technology, 28, 302–312

Lee, J. J. and Cho, S. (2004). Problems of special causes in feedback adjustment, Journal of the Korean Society for Quality Improvement, 32, 201–211

Lu, C. W. and Reynolds, M. R. (2001). CUSUM charts for monitoring an autocorrelated processes, Journal of Quality Technology, 33, 316–334

Lucas, J. L. (1982). Combined Shewhart-CUSUM quality control schemes, Journal of Quality Technology, 14, 51–59

Montgomery, D. C. (2009). Statistical Quality Control: A Modern Introduction, 6th ed., Wiley, New York

Runger, G. C. and Prabhu, S. S. (1996). A Markov chain models for the multivariate exponential weighted moving average control chart, Journal of the American Statistical Association, 91, 1701–1706

Shu, L. and Jiang, W. (2006). A Markov chain model for the adaptive CUSUM control chart, Journal of Quality Technology, 38, 135–147

Shu, L., Jiang, W. and Tsui, K-L. (2008). A weighted CUSUM charts for detecting patterned mean shifts, Journal of Quality Technology, 40, 194–213

Sparks, R. S. (2000). CUSUM charts for signaling varying location shifts, Journal of Quality Technology, 32, 157–171

Tsung, F. and Tsui, K-L. (2003). A mean shift pattern study on integration of SPC and APC for process monitoring, IIE Transactions, 35, 231–242 crossref(new window)

Vander W. S., Tucker, W. T., Faltin, F. W. and Doganaksov, N. (1992). Algorithmic statistical process control: concepts and an application, Technometrics, 34, 286–297

Wardell, D. G., Moskowitz, H. and Plante, R. D. (1994). Run-length distributions of special-cause control charts for correlated processes, Technometrics, 36, 3–17