Diagnosing Problems of Distribution-Free Multivariate Control Chart

Wen Li Shi; Xue Min Zi

doi:10.4028/www.scientific.net/AMR.971-973.1602

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 971-973Diagnosing Problems of Distribution-Free...

Diagnosing Problems of Distribution-Free Multivariate Control Chart

Abstract:

In order to solve the problem of only have a few historical data that can be used in multivariate process monitoring, a new distribution-free multivariate control chart has been proposed. And in the control chart structure the control limits are determined on-line with the future observations and the historical data. Therefore, the proposed control chart has very important application in practice. However, the research doesn’t study the problem of the fault diagnosis after the control chart alarms. So we use LASSO-based diagnostic framework to identify when a detected shift has occurred and to isolate the shifted components.

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Periodical:

Advanced Materials Research (Volumes 971-973)

Pages:

1602-1606

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.971-973.1602

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Online since:

June 2014

Authors:

Wen Li Shi, Xue Min Zi

Keywords:

Distribution-Free, Fault Diagnosis, Lasso, Multivariate Statistical Process Control

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References

[1] Woodall, W. H. and Montgomery, D. Some Current Directions in the Theory and Application of Statistical Process Monitoring, Journal of Quality Technology, to appear. (2013).

Google Scholar

[2] Nan Chen, Xuemin Zi and Changliang Zou. A Distribution-free Multivariate Control Chart(2014).

Google Scholar

[3] Zou, C., Jiang, W., and Tsung, F. A Lasso-Based Diagnostic Framework for Multivariate Statistical Process Control, Technometrics, 53, 297-309. (2011).

DOI: 10.1198/tech.2011.10034

Google Scholar

[4] Schwarz, G. Estimating the Dimension of a Model, The Annals of Statistics, 6, 461-464. (1978).

Google Scholar

[5] Tibshirani, R. J. Regression Shrinkage and Selection via the LASSO, Journal of the Royal Statistical Society: Series B, 58, 267-288. (1996).

DOI: 10.1111/j.2517-6161.1996.tb02080.x

Google Scholar

[6] Zou, C., and Qiu, P. Multivariate Statistical Process Control Using LASSO, Journal of the American Statistical Association, 104, 1586-1596. (2009).

DOI: 10.1198/jasa.2009.tm08128

Google Scholar

[7] Chen, J., and Chen, Z. Extended Bayesian Information Criterion for Model Selection with Large Model Spaces, Biometrika, 95, 759-771. (2008).

DOI: 10.1093/biomet/asn034

Google Scholar

[8] Zou, H. The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, 101, 1418-1429. (2006).

DOI: 10.1198/016214506000000735

Google Scholar

[9] Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. Least Angle Regression, The Annals of Statistics, 32, 407-489. (2004).

DOI: 10.1214/009053604000000067

Google Scholar