Abstract
Statistical control charts are usually designed to monitor independently distributed observations, typically subject to a normal distribution. For many industrial processes the normal distribution may indeed provide an adequate description of data. When production is in discrete items, the assumption of independence may often be reasonable, whereas many chemical and environmental processes show an inherent dynamical variation with the implication that successive observations are (strongly) correlated.
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References
ALWAN, L. C. and ROBERTS, H. V. (1988). Time-Series Modeling for Statistical Process Control. Journal of Business & Economic Statistics, 6(1), 87–95.
BAGSHAW, M. and JOHNSON, R. A. (1975). The Effect of Serial Correlation on the Performance of CUSUM Tests II. Technometrics, 17(1), 73–80.
BOX, G. E. P. and JENKINS, G. M. (1970). Time Series Analysis, Forecasting, and Control Holden Day, San Francisco.
CROWDER, S. V. (1986). Kalman Filtering and Statistical Process Control. Ph.D. dissertation, Iowa State University.
IWERSEN, J. (1992). Statistical Control Charts: Performance of Shewhart and CUSUM Charts. Ph.D. Thesis No. 63, The Institute of Mathematical Statistics and Operations Research, The Technical University of Denmark.
JAZWINSKI, A. H. (1970). Stochastic Processes and Filtering Theory. Academic Press, New York.
JOHNSON, R. A. and BAGSHAW, M. (1974). The Effect of Serial Correlation on the Performance of CUSUM Tests. Technometrics, 16(1), 103–112.
MEINHOLD, R. J. and SINGPURWALLA, N. D. (1983). Understanding the Kalman Filter. The American Statistician, 37(2), 123–127.
MONTGOMERY, D. C. and MASTRANGELO, C. M. (1991). Some Statistical Process Control Methods for Autocorrelated Data. Journal of Quality Technology, 23(3), 179–204. (With discussion).
YOURSTONE, S. A. and MONTGOMERY, D. C. (1989). A Time-Series Approach to Discrete Real-Time Process Quality Control. Quality and Reliability Engineering International, 5, 309–317.
YOURSTONE, S. A. and MONTGOMERY, D. C. (1991). Detection of Process Upsets — Sample Autocorrelation Control Chart and Group Autocorrelation Control Chart Applications. Quality and Reliability Engineering International, 7, 133–140.
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© 1997 Springer-Verlag Berlin Heidelberg
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Iwersen, J. (1997). Statistical Process Control for Autocorrelated Processes: A Case-Study. In: Lenz, HJ., Wilrich, PT. (eds) Frontiers in Statistical Quality Control. Frontiers in Statistical Quality Control, vol 5. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-59239-3_11
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DOI: https://doi.org/10.1007/978-3-642-59239-3_11
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0984-8
Online ISBN: 978-3-642-59239-3
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