Online monitoring of profiles via RVC model with an application to industrial busbar

References

• Amiri, A., Yeh, A. B., & Asgari, A. (2016). Monitoring two-stage processes with binomial data using generalized linear model-based control charts. Quality Technology and Quantitative Management, 13(3), 241–262. https://doi.org/10.1080/16843703.2016.1189013
   Web of Science ®Google Scholar
• Driscoll, A. R., Woodall, W. H., & Zou, C. (2021). Use of conditional false alarm metric in statistical process monitoring. Frontiers in Statistical Quality Control, 13, 3–12. https://doi.org/10.1007/978-3-030-67856-2_1
   Google Scholar
• Fan, J., & Zhang, J. T. (2000). Two-step estimation of functional linear models with applications to longitudinal data. Journal of the Royal Statistical Society Series B: Statistical Methodology, 62(2), 303–322. https://doi.org/10.1111/1467-9868.00233
   Google Scholar
• Gomaa, A. S., & Birch, J. B. (2019). A semiparametric nonlinear mixed model approach to phase I profile monitoring. Communications in Statistics - Simulation & Computation, 48(6), 1677–1693. https://doi.org/10.1080/03610918.2017.1422751
   Web of Science ®Google Scholar
• Guo, W. (2004). Functional data analysis in longitudinal settings using smoothing splines. Statistical Methods in Medical Research, 13(1), 49–62. https://doi.org/10.1191/0962280204sm352ra
   PubMed Web of Science ®Google Scholar
• Jensen, W. A., & Birch, J. B. (2009). Profile monitoring via nonlinear mixed models. Journal of Quality Technology, 41(1), 18–34. https://doi.org/10.1080/00224065.2009.11917757
   Web of Science ®Google Scholar
• Jensen, W. A., Birch, J. B., & Woodall, W. H. (2008). Monitoring correlation within linear profiles using mixed models. Journal of Quality Technology, 40(2), 167–183. https://doi.org/10.1080/00224065.2008.11917723
   Web of Science ®Google Scholar
• Jensen, W. A., Jones-Farmer, L. A., Champ, C. W., & Woodall, W. H. (2006). Effects of parameter estimation on control chart properties: A literature review. Journal of Quality Technology, 38(4), 349–364. https://doi.org/10.1080/00224065.2006.11918623
   Web of Science ®Google Scholar
• Kokoszka, P., & Reimherr, M. (2017). Introduction to functional data analysis. Chapman and Hall/CRC.
   Google Scholar
• Liang, H., Wu, H., & Carroll, R. J. (2003). The relationship between virologic and immunologic responses in AIDS clinical research using mixed-effects varying-coefficient models with measurement error. Biostatistics, 4(2), 297–312. https://doi.org/10.1093/biostatistics/4.2.297
   PubMed Web of Science ®Google Scholar
• Liu, P., Du, J., Zang, Y., Zhang, C., & Wang, K. (2023). In-profile monitoring for cluster-correlated data in advanced manufacturing system. Journal of Quality Technology, 55(2), 195–219. https://doi.org/10.1080/00224065.2022.2106912
   Web of Science ®Google Scholar
• Liu, B., Wang, L., & Cao, J. (2017). Estimating functional linear mixed-effects regression models. Computational Statistics & Data Analysis, 106, 153–164. https://doi.org/10.1016/j.csda.2016.09.009
   Web of Science ®Google Scholar
• Mahmoud, M. A., & Woodall, W. H. (2004). Phase I analysis of linear profiles with calibration applications. Technometrics, 46(4), 380–391. https://doi.org/10.1198/004017004000000455
   Web of Science ®Google Scholar
• Müller, H. G. (2012). Nonparametric regression analysis of longitudinal data. Springer Science & Business Media.
   Google Scholar
• Niaki, S. T. A., Khedmati, M., & Soleymanian, M. E. (2015). Statistical monitoring of autocorrelated simple linear profiles based on principal components analysis. Communications in Statistics-Theory and Methods, 44(21), 4454–4475. https://doi.org/10.1080/03610926.2013.835417
   Web of Science ®Google Scholar
• Nie, B., Liu, D., Liu, X., & Ye, W. (2021). Phase I non-linear profiles monitoring using a modified hausdorff distance algorithm and clustering analysis. International Journal of Quality & Reliability Management, 38(2), 536–550. https://doi.org/10.1108/IJQRM-01-2020-0001
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1002/qre.1066
   Web of Science ®Google Scholar
• Quevedo, A. V., & Vining, G. G. (2022). Online monitoring of nonlinear profiles using a gaussian process model with heteroscedasticity. Quality Engineering, 34(1), 58–74. https://doi.org/10.1080/08982112.2021.1998530
   Web of Science ®Google Scholar
• Shen, X., Zou, C., Jiang, W., & Tsung, F. (2013). Monitoring poisson count data with probability control limits when sample sizes are time varying. Naval Research Logistics, 60(8), 625–636. https://doi.org/10.1002/nav.21557
   Web of Science ®Google Scholar
• Siddiqui, Z., & Abdel‐Salam, A. S. G. (2019). A semiparametric profile monitoring via residuals. Quality and Reliability Engineering International, 35(4), 959–977. https://doi.org/10.1002/qre.2439
   Web of Science ®Google Scholar
• Song, L., He, S., Li, T., & Shang, Y. (2022). Semiparametric control schemes for dynamically monitoring profiles with count data and arbitrary design. International Journal of Production Research, 61(4), 1185–1201. https://doi.org/10.1080/00207543.2022.2030066
   Web of Science ®Google Scholar
• Steiner, S., Jensen, W. A., & Grimshaw, S. D. (2016). Nonlinear profile monitoring for oven-temperature data. Journal of Quality Technology, 48(1), 84–97. https://doi.org/10.1080/00224065.2016.11918153
   Web of Science ®Google Scholar
• Wang, F. K. (2016). Process yield analysis for multivariate linear profiles. Quality Technology & Quantitative Management, 13(2), 124–138. https://doi.org/10.1080/16843703.2016.1169676
   Web of Science ®Google Scholar
• Wang, L., Liu, X., Liang, H., & Carroll, R. J. (2011). Estimation and variable selection for generalized additive partial linear models. The Annals of Statistics, 39(4), 1827–1851. https://doi.org/10.1214/11-AOS885
   PubMed Web of Science ®Google Scholar
• Wu, H., & Liang, H. (2004). Backfitting random varying-coefficient models with time-dependent smoothing covariates. Scandinavian Journal of Statistics, 31(1), 3–19. https://doi.org/10.1111/j.1467-9469.2004.00369.x
   Web of Science ®Google Scholar
• Wu, H., & Zhang, J. T. (2006). Nonparametric regression methods for longitudinal data analysis: Mixed-effects modeling approaches. John Wiley & Sons.
   Google Scholar
• Xiang, D., Mukherjee, A., Hu, Z., & Li, W. (2021). Online anomaly detection of profiles with varying coefficients via functional mixed effects modelling. Applied Mathematical Modelling, 93, 467–484. https://doi.org/10.1016/j.apm.2020.11.039
   Web of Science ®Google Scholar
• Yang, W., Zou, C., & Wang, Z. (2017). Nonparametric profile monitoring using dynamic probability control limits. Quality and Reliability Engineering International, 33(5), 1131–1142. https://doi.org/10.1002/qre.2104
   Web of Science ®Google Scholar
• Yeganeh, A., Parvizi Amineh, M., Shadman, A., Shongwe, S. C., & Mohasel, S. M. (2023). Combination of sequential sampling technique with GLR control charts for monitoring linear profiles based on the random explanatory variables. Mathematics, 11(7), 1683. https://doi.org/10.3390/math11071683
   Web of Science ®Google Scholar
• Yeh, A. B., Huwang, L., & Li, Y. M. (2009). Profile monitoring for a binary response. IIE Transactions, 41(11), 931–941. https://doi.org/10.1080/07408170902735400
   Web of Science ®Google Scholar
• Zaidi, F. S., Dai, H. L., Imran, M., & Tran, K. P. (2023). Monitoring autocorrelated compositional data vectors using an enhanced residuals hotelling T2 control chart. Computers & Industrial Engineering, 181, 109280. https://doi.org/10.1016/j.cie.2023.109280
   Web of Science ®Google Scholar