Proposal of methodology for practical application of nonparametric control charts

• Autorzy: Noskievičová Darja , Smajdorová Tereza

Identyfikatory

DOI

10.2478/cqpi-2019-0063

• Języki publikacji: EN

Abstrakty

EN

This paper deals with the methodology for practical application of nonparametric control charts. This topic is very important for two reasons: firstly nonparametric control charts are very effective instruments for the realization of the statistical process monitoring phase I due to their robustness against various deviations from the data assumptions that must be met when applying model-based control charts. Secondly nonparametric control charts have very weak SW support and also they are not taught in the frame of training courses not even of the university study programmes. For that reason the practitioners do not know them and do not use them. The paper offers the proposal how to practically apply these control charts which is based on the complex simulation study of various nonparametric control charts performance when various data assumptions have not been met. The study has covered these nonparametric control charts: Shewhart sign control chart, nonparametric EWMA and nonparametric CUSUM control charts, nonparametric progressive mean control chart, control chart based on Mood statistics and robust median absolute deviation control chart. All charts have been studied in condition of not normally distributed data, autocorrelated data and data with nonconstant distribution parameters. The simulations were realized for statistically stable (IC – in control) and also statistically unstable (OC – out of control) processes. For the evaluation of the control charts performance median run length, 0.05-quantile, and 0.95-quantile were used.

Słowa kluczowe

EN

statistical process monitoring; nonparametric control charts; median run length; simulation; quantiles

PL

statystyczne monitorowanie procesów; nieparametryczne wykresy kontrolne; mediana długości przebiegu; symulacja; kwantyle

• Wydawca: De Gruyter
• Czasopismo: Quality Production Improvement - QPI
• Rocznik: 2019
• Tom: Vol. 1, Iss. 1
• Strony: 464--471
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Noskievičová Darja

• VŠB-TU Ostrava, Czech Republic

autor

Smajdorová Tereza

• VŠB-TU Ostrava, Czech Republic

Bibliografia

• 1.Abbas, N., Zafar, R. F., Riaz, M., Hussain, Z., 2013. Progressive Mean Control Chart for Monitoring Process Location Parameter, Quality and Reliability Engineering International, 29(3), 357-367, DOI: 10.1002/qre.1386. ISSN 07488017.
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• 3.Bush, H.M., Chongfuangprinya, P., Chen, V. C., Sukchotrat, T., Kim, S. B., 2010. Nonparametric multivariate control charts based on a linkage ranking algorithm, Quality and Reliability Engineering International, 26(7), 663-675, DOI: 10.1002/qre.1129.
• 4.Capizzi, G., 2015. Recent advantages in process monitoring: Nonparametric and variable-selection methods for phase I and II. Quality Engineering, 27, 44-67, DOI: 10.1080/08982112.2015.968046.
• 5.Chakraborti, S., Van Der Laan, P., Bakir, S. T., 2001. Nonparametric control charts : an overview and some results, Journal of Quality Technology, 33(3), 304-315 [cit. 2017-11-06].
• 6.Chakraborti, S., Van De Wiel, M. A., 2008. A nonparametric control chart based on the Mann-Whitney statistic. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen Beachwood, Ohio, USA: Institute of Mathematical Statistics, 156, DOI: 10.1214/193940307000000112.
• 7.Chin, W. S., Khoo, M. B. C., 2012. A study of the median run length (MRL) Performance of the EWMA T chart for the mean. The South African Journal of Industrial Engineering, 23(3), 42-55, DOI: 10.7166/23-3-510
• 8.Coelho, M., Chakraborti, S., Marien, A., Graham, M. A., 2015. A comparison of phase i control charts. The South African Journal of Industrial Engineering, 26(2), 178- 190, DOI: 10.7166/26-2-1026
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• 11.Graham, M. A., Chakraborti, S., Mukherjee, A., 2014. Design and implementation of CUSUM exceedance control charts for unknown location. International Journal of Production Research, 52(18), DOI: 10.1080/00207543.2014.917214.
• 12.Graham, M. A., Chakraborti, S., Human, S. W., 2011. A nonparametric exponentially weighted moving average signed-rank chart for monitoring location. Computational Statistics & Data Analysis, 55(8), DOI: 10.1016/j.csda.2011.02.013.
• 13.Lu, S. L., 2015. An Extended Nonparametric Exponentially Weighted Moving Average Sign Control Chart. Quality and Reliability Engineering International, 31(1), 3-13, DOI: 10.1002/qre.1673.
• 14.Murakami, H. T. Matsuki, 2010. A nonparametric control chart based on the Mood statistic for dispersion, The International Journal of Advanced Manufacturing Technology, 49(5-8), 757-763, DOI: 10.1007/s00170-009-2439-3.
• 15.Oprime, P.C., de Toledo, J. C., Gonzáles, M. O. A., Chakraborti, S., 2016. Methods for determining the control limits of nonparametric charts for monitoring location and scale, Gest Prod., 23(1), 146-164.
• 16.Smajdorová, T., 2019. Application of nonparametric methods in statistical process control (in Czech), Disertation thesis. Ostrava, VŠB-TUO, Czech Republic, 185.
• 17.Wang, D., L. Zhang, Q., Xiong, Q., 2016. A non parametric CUSUM control chart based on the Mann–Whitney statistic. Communications in Statistics - Theory and Methods, 46(10), DOI: 10.1080/03610926.2015.1073314
• 18.Woodal, W. H., 2017. Bridging the gap between theory and practice in basic statistical process monitoring, Quality Engineering, 29(1,2-15), 1532-1547, DOI: 10.1080/08982112.2016.1210449
• 19.Yang, S. F., Lin, J. S., Cheng, S. W., 2011. A new nonparametric EWMA sign control chart, Expert Systems with Applications, 38(5), DOI: 10.1016/j.eswa.2010.11.044

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).