Buradasınız

Anasayfa » Content

Univariate and multivariate control charts for monitoring dynamic-behavior processes: a case study

Journal Name:

  • Journal of Industrial Engineering and Management

Publication Year:

  • 2009
DOI: 
doi:10.3926/jiem.2009.v2n3.p464-498

Key Words:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
Abstract: The majority of classic SPC methodologies assume a steady-state (i.e., static) process behavior (i.e., the process mean and variance are constant) without the influence of the dynamic behavior (i.e., an intended or unintended shift in the process mean or variance). Traditional SPC has been successfully used in steady-state manufacturing processes, but these approaches are not valid for use in dynamic behavior environments. The goal of this paper is to present the process monitoring and adjustment methodologies for addressing dynamic behavior problems so that system performance improvement may be attained. The methodologies will provide a scientific approach to acquire critical knowledge of the dynamic behavior as well as improved control and quality, leading to the enhancement of economic position. The two major developments in this paper are: (1) the characterization of the dynamic behavior of the manufacturing process with the appropriate monitoring procedures; and (2) the development of adaptive monitoring procedures for the processes [for example, using trend charts (e.g., linear model) and time series charts (e.g., ARIMA models)] with a comparison between univariate and multivariate control charts. To provide a realistic environment for the development of the dynamic behavior monitoring and adjustment procedures, the cold rolling process is adopted as a test bed.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-univariate-and-multivariate-control-charts-monitoring-dynamic-behavior-processes-case-study.pdf
  • 3
464-498
  • English

REFERENCES

References: 

Aerne, L. A., Champ, C. W., & Rigdon, S. E. (1991). Evaluation of Control Charts under Linear Trend. Communications in Statistics, 20, 334-3349.
Alt, F. B. (1985). Multivariate Quality Control. Encyclopedia of Statistical Sciences, 6, 110-122.
Alwan, L. C. (1992). Effects of Autocorrelation on Chart Performance. Communications in Statistics - Theory and Methodology, 21, 1025-1049.
Alwan, L. C., & Roberts, H. V. (1988). Time Series Modeling for Statistical Process Control. Business and Economic Statistics, 6, 87-95.
Bissell, A. F. (1984). Estimating of Linear Trend from a Cusum Chart or Tabulation. Applied Statistics, 33, 152-157.
Blank, R. E. (1988). Multivariate X-bar and R Charting Techniques. Presented at the ASQC’s Annual Quality Congress Transactions.
Box, G. E., & Jenkins, G. M. (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day, Oakland, CA.
Box, G. E., Jenkins, G. M., & Reinsel, G. C. (1994). Time Series Analysis, Forecasting and Control. 3rd Ed. Englewood Cliffs, NJ: Prentice-Hall.
Box, G. E., & Kramer, T. (1992). Statistical Process Monitoring and Feedback Adjustment - A Discussion. Technometrics, 34, 251-257.
Box, G. E., & Luceno, A. (1997). Statistical Control by Monitoring and Feedback Adjustment. New York: John Wiley & Sons.
Coleman, D. E. (1997). Individual Contributions in “A Discussion on Statistically– Based Process Monitoring and Control” edited by Montgomery, D.C. & Woodall W.H. ,Journal of Quality Technology 29, 148–149
Cook, D., & Weisberg, S. (1982). Criticism and Influence Analysis in Regression. Sociological Methodology, 13, 313-361.
doi:10.3926/jiem.2009.v2n3.p464-498 ©© JIEM, 2009 – 2(3): 464-498 - ISSN: 2013-0953
Univariate and multivariate control charts for monitoring… 497
S. Haridy; Z. Wu
Elsayed, B. A. (2000). Perspectives and Challenges for Research in Quality and Reliability Engineering. Production Research, 38, 1953-1976.
Farnum, N. R., & Stanton L. W. (1989). Quantitative Forecasting Methods. Boston, MA: PWS-KENT Co.
Grant, E. L., & Leavenworth, R. S. (1996). Statistical Quality Control, 7th Ed. New York: McGraw-Hill, Inc.
Harris, T. J., & Ross, W. H. (1991). Statistical Process Control Procedures for Correlated Observations. The Canadian Journal of Chemical Engineering, 69, 48-57.
Hotelling, H. (1947). Multivariate Quality Control: Techniques of Statistical Analysis. New York: McGraw Hill.
Hunter, J. S. (1986). The Exponentially Weighted Moving Average. Quality Technology, 18, 203-210.
Jackson, J. E. (1956). Quality Control Methods for Two Related Variables. Industrial Quality Control, XII, 2-6.
Jackson, J. E. (1959). Quality Control Methods for Several Related Variables. Technometrics, 1, 359-377.
Lowry, C. A., Woodall, W. H., Champ, C. W., & Rigdon, S. E. (1992). A Multivariate Exponentially Weighted Moving Average Control Chart. Technometrics, 34, 46-53.
Lu, C. W., & Reynolds, J. M. (1999). EWMA Control Charts for Monitoring the Mean of Autocorrelated Processes. Quality Technology, 31, 166-187.
Martinich, J. S. (1997). Production and Operations Management. New York: John Wiley & Sons, Inc.
Montgomery, D. C. (2005). Introduction to Statistical Quality Control, 5th Ed. New York: John Wiley & Sons.
Montgomery, D. C., & Runger, G. C. (2003). Applied Statistics and Probability for Engineers, 3rd Ed. New York: John Wiley & Sons.
doi:10.3926/jiem.2009.v2n3.p464-498 ©© JIEM, 2009 – 2(3): 464-498 - ISSN: 2013-0953
Univariate and multivariate control charts for monitoring… 498
S. Haridy; Z. Wu
Montgomery, D. C., & Wadsworth, H. M. (1972). Some Techniques for Multivariate Quality Control Applications. Presented at the ASQC’s Annual Technical Conference Transactions. Washington, D. C.
Morrison, D. F. (1990). Multivariate statistical Methods, 3rd Ed. New York: McGraw-Hill, Inc.
Nembhard, H. B., & Mastrangelo, C. M. (1998). Integrated Process Control for Startup Operations. Journal of Quality Technology, 30, 201-211.
Nembhard, H. B., Mastrangelo, C. M., & Kao, M. S. (2001). Statistical Monitoring Performance for Startup Operations in a Feedback Control System. Quality and Reliability Engineering International, 17, 379-390.
Ogunnaike, B. A., & Ray, H. W. (1994). Process Dynamics, Modeling and Control. Oxford University Press, Oxford: McGraw-Hill, Inc.
Reed-Hill, R. A. (1994). Physical Metallurgy Principles, 3rd Ed. Boston: PWS Publishing Company.
Roberts, S. W. (1959). Control Chart Tests Based on Geometric Moving Average. Technometrics, 1, 239-250.
Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. New York: D. Van Nostrand Company.
Sultan, T. I. (1986). An Acceptance Chart for Raw Materials of Two Correlated Properties. Quality Assurance, 12, 70-72
VanBrackle, L. N., & Reynolds, J. M. (1997). EWMA and CUSUM Control Charts in the Presence of Correlation. Communications in Statistics - Simulation, 26, 979-1008.

Thank you for copying data from http://www.arastirmax.com