References
- Ahsan, M., Mashuri, M., Lee, M.H., Kuswanto, H., & Prastyo, D.D. (2020). Robust adaptive multivariate Hotellingotelling’s T2 control chart based on kernel density estimation for intrusion detection system. Expert Systems with Applications, 145, 113105. https://doi.org/10.1016/j.eswa.2019.113105
- Colosimo, B.M., Cicorella, P., Pacella, M., & Blaco, M. (2014). From profile to surface monitoring: SPC for cylindrical surfaces via Gaussian processes. Journal of Quality Technology, 46(2), 95–113. https://doi.org/10.1080/00224065.2014.11917956
- Ding, N., He, Z., Shi, L., & Qu, L. (2021). A new risk‐adjusted EWMA control chart based on survival time for monitoring surgical outcome quality. Quality and Reliability Engineering International, 37(4), 1650–1663. https://doi.org/10.1002/qre.2818
- Fallahdizcheh, A., & Wang, C. (2022). Profile monitoring based on transfer learning of multiple profiles with incomplete samples. IISE Transactions, 54(7), 643–658.
- Ghosh, M., Li, Y., Zeng, L., Zhang, Z., & Zhou, Q. (2021). Modeling multivariate profiles using Gaussian process-controlled B-splines. IISE Transactions, 53(7), 787–798. https://doi.org/10.1080/24725854.2020.1798038
- Guevara, R.D., Vargas, J.A., & Castagliola, P. (2016). Evaluation of process capability in non-linear profiles using Hausdorff distance. Quality Technology & Quantitative Management, 13(1), 1–15. https://doi.org/10.1080/16843703.2016.1139841
- Huwang, L., Wu, C.H., & Lee, Y.R. (2021). EWMA and adaptive EWMA variable sampling intervals charts for simultaneous monitoring of Weibull parameters. Quality Technology & Quantitative Management, 18(5), 552–575. https://doi.org/10.1080/16843703.2021.1918439
- Jiang, Q., Yan, X., & Huang, B. (2015). Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and Bayesian inference. IEEE Transactions on Industrial Electronics, 63(1), 377–386. https://doi.org/10.1109/TIE.2015.2466557
- Jin, R., Chang, C.J., & Shi, J. (2012). Sequential measurement strategy for wafer geometric profile estimation. IIE Transactions, 44(1), 1–12. https://doi.org/10.1080/0740817X.2011.557030
- Kang, L., & Albin, S.L. (2000). On-line monitoring when the process yields a linear profile. Journal of Quality Technology, 32(4), 418–426. https://doi.org/10.1080/00224065.2000.11980027
- Khalili, S., & Noorossana, R. (2022). Online monitoring of autocorrelated multivariate linear profiles via multivariate mixed models. Quality Technology & Quantitative Management, 19(3), 319–340. https://doi.org/10.1080/16843703.2021.2015834
- Li, Y., Zhou, Q., Huang, X., & Zeng, L. (2018). Pairwise estimation of multivariate Gaussian process models with replicated observations: Application to multivariate profile monitoring. Technometrics, 60(1), 70–78. https://doi.org/10.1080/00401706.2017.1305298
- 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
- Maleki, M.R., Amiri, A., & Castagliola, P. (2018). An overview on recent profile monitoring papers (2008–2018) based on conceptual classification scheme. Computers & Industrial Engineering, 126, 705–728. https://doi.org/10.1016/j.cie.2018.10.008
- Montgomery, D.C. (2020). Introduction to statistical quality control. John Wiley & Sons.
- Nasiri Boroujeni, M., Samimi, Y., & Roghanian, E. (2022). Parametric and non-parametric methods for monitoring nonlinear fuzzy profiles. International Journal of Advanced Manufacturing Technology, 118(1), 67–84. https://doi.org/10.1007/s00170-021-07187-z
- Pacella, M., Grieco, A., & Blaco, M. (2017). Machine vision based quality control of free-form profiles in automatic cutting processes. Computers & Industrial Engineering, 109, 221–232. https://doi.org/10.1016/j.cie.2017.04.039
- Peres, F.A.P., & Fogliatto, F.S. (2018). Variable selection methods in multivariate statistical process control: A systematic literature review. Computers & Industrial Engineering, 115, 603–619. https://doi.org/10.1016/j.cie.2017.12.006
- Plumlee, M., Jin, R., Roshan Joseph, V., & Shi, J. (2013). Gaussian process modeling for engineered surfaces with applications to Si wafer production. Stat, 2(1), 159–170. https://doi.org/10.1002/sta4.26
- Qiu, P. (2013). Introduction to statistical process control. CRC press.
- Qiu, P. (2018). Some perspectives on nonparametric statistical process control. Journal of Quality Technology, 50(1), 49–65. https://doi.org/10.1080/00224065.2018.1404315
- Rasmussen, C.E., & Williams, C.K.I. (2006). Gaussian processes for machine learning. MIT Press.
- Ren, L., Cui, J., Sun, Y., & Cheng, X. (2017). Multi-bearing remaining useful life collaborative prediction: A deep learning approach. Journal of Manufacturing Systems, 43, 248–256. https://doi.org/10.1016/j.jmsy.2017.02.013
- Sachlas, A., Bersimis, S., & Psarakis, S. (2019). Risk-adjusted control charts: Theory, methods, and applications in health. Statistics in Biosciences, 11(3), 630–658. https://doi.org/10.1007/s12561-019-09257-z
- Saha, S., Mukherjee, A., & Khoo, M.B. (2022). Two CUSUM schemes for simultaneous monitoring of unknown parameters of a shifted exponential process and its application in monitoring of call durations in telemarketing. Quality Technology & Quantitative Management, 19(1), 113–137. https://doi.org/10.1080/16843703.2021.1993567
- Shpak, A. (1995). Global optimization in one-dimensional case using analytically defined derivatives of objective function. Computer Science Journal of Moldova, 3(2), 168–184.
- Varbanov, R., Chicken, E., Linero, A., & Yang, Y. (2019). A Bayesian approach to sequential monitoring of nonlinear profiles using wavelets. Quality and Reliability Engineering International, 35(3), 761–775. https://doi.org/10.1002/qre.2409
- Woodall, W.H. (2007). Current research on profile monitoring. Production, 17(3), 420–425. https://doi.org/10.1590/S0103-65132007000300002
- Woodall, W.H., & Montgomery, D.C. (2014). Some current directions in the theory and application of statistical process monitoring. Journal of Quality Technology, 46(1), 78–94. https://doi.org/10.1080/00224065.2014.11917955
- Woodall, W.H., Spitzner, D.J., Montgomery, D.C., & Gupta, S. (2004). Using control charts to monitor process and product quality profiles. Journal of Quality Technology, 36(3), 309–320. https://doi.org/10.1080/00224065.2004.11980276
- Xia, Z., & Tsung, F. (2019). A computationally efficient self-starting scheme to monitor general linear profiles with abrupt changes. Quality Technology & Quantitative Management, 16(3), 278–296. https://doi.org/10.1080/16843703.2017.1396956
- Yang, K., & Qiu, P. (2021). Adaptive process monitoring using covariate information. Technometrics, 63(3), 313–328. https://doi.org/10.1080/00401706.2020.1772115
- Yeganeh, A., Shadman, A., & Amiri, A. (2021). A novel run rules based MEWMA scheme for monitoring general linear profiles. Computers & Industrial Engineering, 152, 107031. https://doi.org/10.1016/j.cie.2020.107031
- Zeng, L., Neogi, S., & Zhou, Q. (2014). Robust Phase I monitoring of profile data with application in low-E glass manufacturing processes. Journal of Manufacturing Systems, 33(4), 508–521. https://doi.org/10.1016/j.jmsy.2014.05.001
- Zhang, C., Lei, Y., Zhang, L., & Chen, N. (2017). Modeling tunnel profile in the presence of coordinate errors: A Gaussian process-based approach. IISE Transactions, 49(11), 1065–1077. https://doi.org/10.1080/24725854.2017.1348646
- Zhang, L., Wang, K., & Chen, N. (2016). Monitoring wafers’ geometric quality using an additive Gaussian process model. IIE Transactions, 48(1), 1–15. https://doi.org/10.1080/0740817X.2015.1027455
- Zhou, Q., & Qiu, P. (2022). Phase I monitoring of serially correlated nonparametric profiles by mixed‐effects modeling. Quality and Reliability Engineering International, 38(1), 134–152. https://doi.org/10.1002/qre.2961