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Journal of Quality Technology
A Quarterly Journal of Methods, Applications and Related Topics
Volume 55, 2023 - Issue 2
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In-profile monitoring for cluster-correlated data in advanced manufacturing system

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References

  • Anderson, B. D. O., J. B. Moore, and M. Eslami. 1982. Optimal filtering. IEEE Transactions on Systems, Man, and Cybernetics 12 (2):235–6. doi: 10.1109/TSMC.1982.4308806.
  • Ando, R. K, and T. Zhang. 2007. Learning on graph with Laplacian regularization. In Advances in neural information processing systems, 25–32.
  • Barber, D. 2012. Bayesian reasoning and machine learning. Cambridge: Cambridge University Press.
  • Bartels, R. H, and G. W. Stewart. 1972. Solution of the matrix equation ax + xb = c [f4]. Communications of the ACM 15 (9):820–6. doi: 10.1145/361573.361582.
  • Berndt, D. J, and J. Clifford. 1994. Using dynamic time warping to find patterns in time series. in KDD workshop, Seattle, WA, vol. 10, 359–70.
  • Du, J., X. Zhang, X. Xu, and J. Shi. 2018. A novel critical point detection method for mechanical deformation in tightening processes. Journal of Manufacturing Systems 48:157–65. doi: 10.1016/j.jmsy.2018.07.007.
  • Durbin, J, and S. J. Koopman. 2012. Time series analysis by state space methods, vol. 38, Oxford: Oxford University Press.
  • Eyvazian, M., R. Noorossana, A. Saghaei, and A. Amiri. 2011. Phase II monitoring of multivariate multiple linear regression profiles. Quality and Reliability Engineering International 27 (3):281–96. doi: 10.1002/qre.1119.
  • Gevers, M., and V. Wertz. 1984. Uniquely identifiable state-space and ARMA parametrizations for multivariable linear systems. Automatica 20 (3):333–47. doi: 10.1016/0005-1098(84)90048-7.
  • Ghahramani, Z., and G. E. Hinton. 1996. Parameter estimation for linear dynamical systems. Technical report, Technical Report CRG-TR-96-2, University of Totronto, Department of Computer Science.
  • Grasso, M., B. Colosimo, and M. Pacella. 2014. Profile monitoring via sensor fusion: the use of PCA methods for multi-channel data. International Journal of Production Research 52 (20):6110–35. doi: 10.1080/00207543.2014.916431.
  • Grasso, M., A. Menafoglio, B. M. Colosimo, and P. Secchi. 2016. Using curve-registration information for profile monitoring. Journal of Quality Technology 48 (2):99–127. doi: 10.1080/00224065.2016.11918154.
  • Hawkins, D. M., and E. M. Maboudou-Tchao. 2007. Self-starting multivariate exponentially weighted moving average control charting. Technometrics 49 (2):199–209. doi: 10.1198/004017007000000083.
  • Henze, N, and B. Zirkler. 1990. A class of invariant consistent tests for multivariate normality. Communications in Statistics - Theory and Methods 19 (10):3595–617. doi: 10.1080/03610929008830400.
  • Jong, P. D. 1991. The diffuse Kalman filter. The Annals of Statistics 19 (2):1073–83. doi: 10.1214/aos/1176348139.
  • Khedmati, M, and S. T. A. Niaki. 2016. Phase II monitoring of general linear profiles in the presence of between-profile autocorrelation. Quality and Reliability Engineering International 32 (2):443–52. doi: 10.1002/qre.1762.
  • Li, Z., N. D. Sergin, H. Yan, C. Zhang, and F. Tsung. 2020. Tensor completion for weakly-dependent data on graph for metro passenger flow prediction. Proceedings of the AAAI Conference on Artificial Intelligence 34 (04):4804–10. doi: 10.1609/aaai.v34i04.5915.
  • Liu, Z, and M. Hauskrecht. 2013. Sparse linear dynamical system with its application in multivariate clinical time series.
  • Liu, J., J. Shi, and S. J. Hu. 2009. Quality-assured setup planning based on the stream-of-variation model for multi-stage machining processes. IIE Transactions 41 (4):323–34. doi: 10.1080/07408170802108526.
  • Nomikos, P, and J. F. MacGregor. 1995. Multivariate SPC charts for monitoring batch processes. Technometrics 37 (1):41–59. doi: 10.1080/00401706.1995.10485888.
  • Noorossana, R., M. Eyvazian, A. Amiri, and M. A. Mahmoud. 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.
  • Overschee, P. V, and B. D. Moor. 1996. Subspace identification for linear systems: theory, implementation, applications. New York: Kluwer.
  • Pan, X, and J. Jarrett. 2007. Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlation. International Journal of Production Economics 106 (1):204–16. doi: 10.1016/j.ijpe.2006.07.002.
  • Paynabar, K., J. J. Jin, and M. Pacella. 2013. Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis. IIE Transactions 45 (11):1235–47. doi: 10.1080/0740817X.2013.770187.
  • Paynabar, K., C. Zou, and P. Qiu. 2016. A change-point approach for Phase-I analysis in multivariate profile monitoring and diagnosis. Technometrics 58 (2):191–204. doi: 10.1080/00401706.2015.1042168.
  • Qiu, P. 2008. Distribution-free multivariate process control based on log-linear modeling. IIE Transactions 40 (7):664–77. doi: 10.1080/07408170701744843.
  • Qiu, P, and D. Xiang. 2014. Univariate dynamic screening system: an approach for identifying individuals with irregular longitudinal behavior. Technometrics 56 (2):248–60. doi: 10.1080/00401706.2013.822423.
  • Qiu, P., X. Zi, and C. Zou. 2018. Nonparametric dynamic curve monitoring. Technometrics 60 (3):386–97. doi: 10.1080/00401706.2017.1361340.
  • Ramsay, J, and B. Silverman. 2005. Functional data analysis. New York: Springer.
  • Rao, N., H.-F. Yu, P. K. Ravikumar, and I. S. Dhillon. 2015. Collaborative filtering with graph information: consistency and scalable methods. in Advances in neural information processing systems, 2107–15.
  • Shi, J. 2006. Stream of variation modeling and analysis for multistage manufacturing processes. Boca Raton: CRC Press.
  • Thrun, S. 2002. Probabilistic robotics. Communications of the ACM 45 (3):52–7. doi: 10.1145/504729.504754.
  • Wu, C. J. 1983. On the convergence properties of the EM algorithm. The Annals of Statistics 11 (1):95–103. doi: 10.1214/aos/1176346060.
  • Xiang, L, and F. Tsung. 2008. Statistical monitoring of multi-stage processes based on engineering models. IIE Transactions 40 (10):957–70. doi: 10.1080/07408170701880845.
  • Zhang, Y., Z. He, C. Zhang, and W. H. Woodall. 2014. Control charts for monitoring linear profiles with within-profile correlation using Gaussian process models. Quality and Reliability Engineering International 30 (4):487–501. doi: 10.1002/qre.1502.
  • Zhang, C., H. Yan, S. Lee, and J. Shi. 2018a. Multiple profiles sensor-based monitoring and anomaly detection. Journal of Quality Technology 50 (4):344–62. doi: 10.1080/00224065.2018.1508275.
  • Zhang, C., H. Yan, S. Lee, and J. Shi. 2018b. Weakly correlated profile monitoring based on sparse multi-channel functional principal component analysis. IISE Transactions 50 (10):878–91. doi: 10.1080/24725854.2018.1451012.
  • Zhang, C., H. Yan, S. Lee, and J. Shi. 2021. Dynamic multivariate functional data modeling via sparse subspace learning. Technometrics 63 (3):370–83. doi: 10.1080/00401706.2020.1800516.
  • Zhang, C., L. Zhang, and N. Chen. 2017. Spectral network approach for multi-channel profile data analysis with applications in advanced manufacturing. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 1709–1713. doi: 10.1109/IEEM.2017.8290183.
  • Zhou, T., H. Shan, A. Banerjee, and G. Sapiro. 2012. Kernelized probabilistic matrix factorization: exploiting graphs and side information. In Proceedings of the SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, 403.
  • Zou, C., X. Ning, and F. Tsung. 2012. LASSO-based multivariate linear profile monitoring. Annals of Operations Research 192 (1):3–19. doi: 10.1007/s10479-010-0797-8.

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