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IIE Transactions Volume 30, 1998 - Issue 6
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ORIGINAL ARTICLES

The development and evaluation of CUSUM-based control charts for an AR(1) process

DOUGLAS H. TIMMER 401 Sunflower Drive, Pflugerville, TX, 78660, USA E-mail: [email protected]
,
JOSEPH PIGNATIELLO JR Department of Industrial Engineering, Texas A &. M University, MS-3131, College Station, TX, 77843-3131, USA E-mail: [email protected]
&
MICHAEL LONGNECKER Department of Statistics, Texas A&M University, MS-3143, College Station, TX, 77843-3143, USA E-mail: [email protected]
Pages 525-534 | Received 01 Sep 1996, Published online: 30 May 2007

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Jianbo Yu & Jianping Liu. (2011) LRProb control chart based on logistic regression for monitoring mean shifts of auto-correlated manufacturing processes. International Journal of Production Research 49:8, pages 2301-2326.
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J. B. Yu & L. F. Xi. (2008) Using an chart based on a self-organizing map NN to monitor out-of-control signals in manufacturing processes. International Journal of Production Research 46:21, pages 5907-5933.
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H. Brian Hwarng. (2004) Detecting process mean shift in the presence of autocorrelation: a neural-network based monitoring scheme. International Journal of Production Research 42:3, pages 573-595.
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Chao-Wen Lu & Marion R. Reynolds$suffix/text()$suffix/text(). (2001) Cusum Charts for Monitoring an Autocorrelated Process. Journal of Quality Technology 33:3, pages 316-334.
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DouglasH. Timmer, JosephJ. Pignatiello$suffix/text()$suffix/text() & MichaelT. Longnecker. (2001) APPLYING AN AR(1) CUSUM CONTROL CHART TO DATA FROM A CHEMICAL PROCESS. Quality Engineering 13:1, pages 107-114.
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Chao-Wen Lu & Marion R. Reynolds$suffix/text()$suffix/text(). (1999) EWMA Control Charts for Monitoring the Mean of Autocorrelated Processes. Journal of Quality Technology 31:2, pages 166-188.
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Kuntal Bera & M. Z. Anis. Estimation of C for autocorrelated data in the presence of random measurement errors. Communications in Statistics - Simulation and Computation 0:0, pages 1-28.
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Articles from other publishers (12)

Aleksey S. Polunchenko & Vasanthan Raghavan. (2018) Comparative performance analysis of the Cumulative Sum chart and the Shiryaev‐Roberts procedure for detecting changes in autocorrelated data. Applied Stochastic Models in Business and Industry 34:6, pages 922-948.
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Suyi Li & Wenjia Wang. (2016) A study on the performance of Lambda Control Chart for autocorrelated processes. A study on the performance of Lambda Control Chart for autocorrelated processes.
Yanhong Wu. (2016) Inference for post-change parameters after sequential CUSUM test under AR(1) model. Journal of Statistical Planning and Inference 168, pages 52-67.
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Víctor G. Tercero-Gómez, Alvaro Cordero-Franco, Angel Pérez-Blanco & Alberto Hernández-Luna. (2014) A Self-Starting CUSUM Chart Combined with a Maximum Likelihood Estimator for the Time of a Detected Shift in the Process Mean. Quality and Reliability Engineering International 30:4, pages 591-599.
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Shichang Du & Jun Lv. (2013) Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes. International Journal of Production Economics 141:1, pages 377-387.
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Mehdi Davoodi & Seyed Taghi Akhavan Niaki. (2012) Estimating the Step-Change Time of the Location Parameter in Multistage Processes Using MLE. Quality and Reliability Engineering International 28:8, pages 843-855.
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Bin Wu & Jian-bo Yu. (2010) A neural network ensemble model for on-line monitoring of process mean and variance shifts in correlated processes. Expert Systems with Applications 37:6, pages 4058-4065.
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A. Fichera & A. Pagano. (2009) Monitoring combustion unstable dynamics by means of control charts. Applied Energy 86:9, pages 1574-1581.
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Fernando A. E. Claro, Antonio F. B. Costa & Marcela A. G. Machado. (2008) Double sampling control chart for a first order autoregressive process. Pesquisa Operacional 28:3, pages 545-562.
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Gonen Singer & Irad Ben‐Gal. (2007) The funnel experiment: The Markov‐based SPC approach. Quality and Reliability Engineering International 23:8, pages 899-913.
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Jamal Arkat, Seyed Taghi Akhavan Niaki & Babak Abbasi. (2007) Artificial neural networks in applying MCUSUM residuals charts for AR(1) processes. Applied Mathematics and Computation 189:2, pages 1889-1901.
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Douglas H. Timmer & Joseph J. Pignatiello. (2003) Change Point Estimates for the Parameters of an AR(1) Process. Quality and Reliability Engineering International 19:4, pages 355-369.
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