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Robust Cluster-Based method for monitoring generalized linear profiles in phase I | ||
Journal of Industrial Engineering International | ||
دوره 17، شماره 1، خرداد 2021، صفحه 88-97 اصل مقاله (771.53 K) | ||
نوع مقاله: Original Article | ||
شناسه دیجیتال (DOI): 10.30495/jiei.2021.1920761.1085 | ||
نویسندگان | ||
Davood Saremian1؛ Rassoul Noorossana* 2؛ Sadigh Raissi1؛ Paria Soleimani1 | ||
1Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran | ||
2Industrial Engineering Department, Iran University of Science and Technology | ||
چکیده | ||
Profile monitoring is one of the new statistical quality control methods used to evaluate the functional relationship between the descriptive and response variables to measure the process quality. Most of the studies in this field concern processes whose response variables follow the normal distribution function, but in many industries and services, this assumption is not true. The presence of outliers in the historical data set could have a deleterious effect on phase I parameter estimation. Therefore, in this paper, we propose a robust cluster-based method for estimating the parameters of generalized linear profiles in phase I. In this method, the effect of data contamination on estimating the generalized linear model parameters is reduced and as a result, the performance of T^2 control charts is improved. The performance of this method has been evaluated for two specific modes of generalized linear profiles, including logistic and Poisson profiles, based on a step shift. The simulation results indicate the superiority of this cluster-based method in comparison to the non-clustering method and provide a more accurate estimation of the parameters. | ||
کلیدواژهها | ||
Generalized Linear Models؛ Phase I؛ Hotelling T^2؛ Clustering؛ Robust | ||
مراجع | ||
[1] Amiri A, Koosha, M., Azhdari & Aand Wang G. (2014). Phase I monitoring of generalized linear model-based regression profiles. Journal of Statistical Computation and Simulation, 85(14), 2839-2859. doi:10.1080/00949655.2014.942864 [2] Aly, A. A., Mahmoud, M. A., & Woodall, W. H. (2014). A
Comparison of the Performance of Phase II Simple Linear Profile Control Charts when Parameters are estimated. Communications in Statistics - Simulation and Computation, 44(6), 1432–1440. doi:10.1080/03610918.2013.821484 [3] Bahiraee Ehsan, Raissi Sadigh, (2014), Economic design of
Hotelling’s T2 control chart on the presence of fixed sampling rate and exponentially assignable causes, Journal of Industrial Engineering International, a Springer Journal, December 2014, Volume 10, Issue 4, pp 229–238, ISSN: 1735-5702. [4] Cantoni, E., & Ronchetti, E. (2001). Robust inference for
generalized linear models. Journal of the American Statistical Association, 96(455), 1022-1030. [5] Change Point Method for Monitoring Generalized Linear
Profiles in Phase I. Quality and Reliability Engineering International, 31(8), 1367-1381. doi:10.1002/qre.1671 [6] Chen, S., & Nembhard, H. B. (2010). A high-dimensional
control chart for profile monitoring. Quality and Reliability Engineering International, 27(4), 451–464. doi:10.1002/ qre.1140 [7] Chen, Y., Birch, J. B., & Woodall, W. H. (2015). Cluster-Based
Profile monitoring in phase 1. Journal of Quality Technology, 47(1), 14-29. doi:10.1080/00224065.2015.11918103 [8] Cheung, J. M. Y., Bartlett, D. J., Armour, C. L., Laba, T. L., & Saini, B. (2018). To drug or not to drug: A qualitative study of patients’ decision-making processes for managing insomnia. Behavioral Sleep Medicine, 16(1), 1-26. doi:10.1080/15402002.2016.1163702 [9] Hakimi, A., Amiri, A., & Kamranrad, R. (2017). Robust approaches for monitoring logistic regression profiles under outliers. International Journal of Quality & Reliability Management, 34(4), 494-507. doi:10.1108/ijqrm-04-2015- 0053 [10] Izadbakhsh, H., Noorossana, R., & Niaki, S. T. A. (2018). Monitoring multinomial logistic profiles in Phase I using log- linear models. International Journal of Quality & Reliability Management, 35(3), 678-689. doi:10.1108/ijqrm-04-2017- 0068 [11] Jensen WA, Birch JB, & Woodall WH. Monitoring Correlation Within Linear Profiles Using Mixed Models. Journal of Quality Technology 2008, 40(2), 167–183. doi:10.1080/00224065. 2008.11917723. [12] Kang, L., & Albin, S. L. (2000). On-Line Monitoring When the Process Yields a Linear Profile. Journal of Quality Technology, 32(4), 418 426. doi:10.1080/00224065.2000. 11980027 [13] Koosha, A. A. a. M. (2011). The Effect of Neglecting Autocorrelation on the Performance of T2 Control Charts in Monitoring of Logistic Profiles. Proceedings of the 2011 IEEE ICQR. [14] Koosha, M., & Amiri, A. (2012). Generalized linear mixed model for monitoring autocorrelated logistic regression profiles. The International Journal of Advanced Manufacturing Technology, 64(1-4), 487-495. doi:10.1007/s00170-012-4018- 2 [15] Li, C.-I., Pan, J.-N., & Liao, C.-H. (2019). Monitoring nonlinear profile data using support vector regression method. Quality and Reliability Engineering International, 35(1), 127-135. doi:https://doi.org/ 10.1002/qre.2385 [16] Moheghi, H. R., Noorossana, R., & Ahmadi, O. (2020). GLM profile monitoring using robust estimators. Quality and Reliability Engineering International, 1– 17.doi:https://doi.org/ 10.1002/qre.2755 [17] McCullagh, P. and Nelder, J.A. (1989) Generalized Linear Models, second edition, Chapman & Hall, London, UK. [18] Nikoo, M., & Noorossana, R. (2012). Phase II Monitoring of Nonlinear Profile Variance Using Wavelet. Quality and Reliability Engineering International, 29(7), 1081-1089. doi:10.1002/qre.1460 [19] Noorossana, R., Eyvazian, M., Amiri, A., & Mahmoud, M. A. (2010). Statistical monitoring of multivariate multiple linear regression profiles in phase I with calibration application. Quality and Reliability Engineering International, 26(3), 291–303. doi:10.1002/qre.1066 [20] Olsson, U. (2002). Generalized Linear Models: An Applied Approach: Lightning Source. [21] Pan, J.-N., Li, C.-I., & Lu, M. Z. (2019). Detecting the process changes for multivariate nonlinear profile data. Quality and Reliability Engineering International, 35(6), 1890-1910. doi:https://doi.org/10.1002/qre.2482 [22] Paynabar, K., Jin, J., & Pacella, M. (2013). Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis. IIE Transactions, 45(11), 1235-1247. doi:10.1080/0740817x. 2013.770187 [23] Shadman, A., Zou, C., Mahlooji, H., & Yeh, A. B. (2014). A change point method for Phase II monitoring of generalized linear profiles. Communications in Statistics - Simulation and Computation, 46(1), 559-578. doi:10.1080/03610918.2014. 970698 [24] Shadman, A., Mahlooji, H., Yeh, A. B., & Zou, C. (2015). A change point method for monitoring generalized linear profiles in phase I. Quality and Reliability Engineering International 31 (8), 1367-1381. https://doi.org/10.1002/qre.1671 [25] Shiau, J.-J. H., & Sun, J.-H. (2009). A new strategy for Phase I analysis in SPC. Quality and Reliability Engineering International, 26(5), 475-486. doi:10.1002/qre.1075 [26] Soleimani Paria, Narvand Ali, Raissi Sadigh, (2013), Online Monitoring of Auto-correlated Linear Profiles via Mixed Model, International Journal of Manufacturing Technology and Management, 27(4/5/6), 238-250. [27] Vargas, N. J. A. (2003). Robust Estimation in Multivariate Control Charts for Individual Observations. Journal of Quality Technology, 35(4), 367–376. doi:10.1080/00224065.2003. 11980234. [28] Walker, E., & Wright, S. P. (2018). Comparing Curves Using Additive Models. Journal of Quality Technology, 34(1), 118-129. doi:10.1080/00224065.2002.11980134 [29] Williams JD, Woodall WH, Birch, JB, & Sullivan JH (2006). Distribution of Hotelling’s T2 Statistic Based on the Successive Differences Estimator. Journal of Quality Technology 2006, 38(3), 217–229. doi:10.1080/00224065. 2006.11918611. [30] YEH, A. B. (2009). profile monitoring for a binary response. IIE Transactions 931–941. doi:10.1080/0740817090273540 | ||
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