Exponentially weighted control charts to monitor multivariate process variability for high dimensions

References

• Banerjee, T. P., and S. Das. 2012. “Multi-sensor Data Fusion Using Support Vector Machine for Motor Fault Detection.” Information Sciences 217: 96–107.10.1016/j.ins.2012.06.016
   Web of Science ®Google Scholar
• Berdjag, D., C. Christophe, V. Cocquempot, and B. Jiang. 2002. “Nonlinear Model Decomposition for Robust Fault Detection and Isolation Using Algebraic Tools.” International Journal of Innovative Computing, Information and Control 2: 1337–1354.
   Google Scholar
• Castanedo, F. 2013. “A Review of Data Fusion Techniques.” The Scientific World Journal 2013 (11): 1–19
   Web of Science ®Google Scholar
• Chen, Y., and K. Hsieh. 2007. “Hotelling’s T2 Charts with Variable Sample Size and Control Limit.” European Journal of Operational Research 182: 1251–1262.10.1016/j.ejor.2006.09.046
   Web of Science ®Google Scholar
• Djauhari, M. A., R. Sagadavan, and L. S. Li. 2016. “Monitoring Multivariate Process Variability When Sub-group Size is Small.” Quality Engineering 28 (4): 429–440.10.1080/08982112.2016.1166386
   Web of Science ®Google Scholar
• Esteban, J., A. Starr, and R. Willetts. 2005. “A Review of Data Fusion Models and Architectures: Towards Engineering Guidelines.” Neural Computing and Applications 14 (4): 273–281.10.1007/s00521-004-0463-7
   Web of Science ®Google Scholar
• Hawkins, D. M. 1974. “The Detection of Errors in Multivariate Data Using Principal Components.” Journal of the American Statistical Association 69: 340–344.10.1080/01621459.1974.10482950
   Web of Science ®Google Scholar
• Hawkins, D. M., and E. M. Maboudou-Tchao. 2008. “Multivariate Exponentially Weighted Moving Covariance Matrix.” Technometrics 50 (2): 155–166.10.1198/004017008000000163
   Web of Science ®Google Scholar
• Huwang, L., A. B. Yeh, and C. Wu. 2007. “Monitoring Multivariate Process Variability for Individual Observations.” Journal of Quality Technology 39 (3): 258–278.
   Web of Science ®Google Scholar
• Hwang, W. Y. 2016. “Chi-square Quantile-based Multivariate Variance Monitoring for Individual Observations.” Communications in Statistics - Simulation and Computation 26 (1): 1532–4141.
   Google Scholar
• Jackson, J. E. 1985. “Multivariate Quality Control.” Communications in Statistics-Theory and Methods 14 (11): 2657–2688.10.1080/03610928508829069
   Web of Science ®Google Scholar
• JDL, Data Fusion Lexicon. 1991. Technical Panel for C3 Code 420. San Diego, CA: F.E. White.
   Google Scholar
• Khaleghi, B., A. Khamis, F. O. Karray, and S. N. Razavi. 2013. “Multisensor Data Fusion: A Review of the State-of-the-Art.” Information Fusion 14 (1): 28–44.10.1016/j.inffus.2011.08.001
   Web of Science ®Google Scholar
• Kim, S. B., W. Jitpitaklert, S. Park, and S. Hwang. 2012. “Data Mining Model-based Control Charts for Multivariate and Autocorrelated Processes.” Expert Systems with Applications 39 (2): 2073–2081.10.1016/j.eswa.2011.08.010
   Web of Science ®Google Scholar
• Llinas, J., and D. L. Hall. 1998. “An Introduction to Multi-sensor Data Fusion.” Proc IEEE International Symposium on Circuits and Systems 6: 537–540.
   Google Scholar
• Lowry, C. A., and D. C. Montgomery. 1995. “A Review of Multivariate Control Charts.” IIE Transactions 27 (6): 800–810.10.1080/07408179508936797
   Web of Science ®Google Scholar
• Luo, R., and C. Yih, and K. Su. 2002. “Multisensor Fusion and Integration: Approaches, Applications, and Future Research Directions.” IEEE Sensors Journal 2: 107–119.10.1109/JSEN.2002.1000251
   Web of Science ®Google Scholar
• Macgregor, J. F., and T. J. Harris. 1993. “The Exponentially Weighted Moving Variance.” Journal of Quality Technology 25 (2): 106–118.
   Web of Science ®Google Scholar
• Mahony, N. O., T. Murphy, K. Panduru, D. Riordan, and J. Walsh. 2016. “Adaptive Process Control and Sensor Fusion for Process Analytical Technology.” Signals and Systems Conference (ISSC), Tralee, June.
   Google Scholar
• Manjunatha, P., A. K. Verma, and A. Srividya. 2008. “Multi-sensor Data Fusion in Cluster Based Wireless Sensor Networks Using Fuzzy Logic Method.” IEEE Region 10 Colloquium and the Third ICIIS, Kharagpur, December 8–10.
   Google Scholar
• Mao, Z., and B. Jiang. 2007. “Fault Identification and Fault-tolerant Control for a Class of Networked Control Systems.” International Journal of Innovation Computing, Information and Control 3: 1121–1130.
   Web of Science ®Google Scholar
• Memar, A. O., and S. T. K. Niaki. 2009. “New Control Charts for Monitoring Covariance Matrix with Individual Observations.” Quality and Reliability Engineering International 25 (7): 821–838.10.1002/qre.v25:7
   Web of Science ®Google Scholar
• Memar, A. O., and S. T. K. Niaki. 2011. “Multivariate Variability Monitoring Using EWMA Control Charts Based on Squared Deviation of Observations from Target.” Quality and Reliability Engineering International 27: 1069–1086.10.1002/qre.1196
   Web of Science ®Google Scholar
• Montgomery, D. C. 2012. Introduction to Statistical Quality Control, 800–810. Hoboken: Wiley.
   Google Scholar
• Olfati-Saber, R. 2007. “Distributed Kalman Filtering for Sensor Networks.” Proceedings of the 46th IEEE conference on Decision and Control, New Orleans, December 12–14.
   Google Scholar
• Rosenthal, J. S. 2000. “Parallel Computing and Monte Carlo Algorithms.” Far East Journal of Theoretical Statistics 4: 207–236.
   Google Scholar
• Safizadeh, M. S., and S. K. Latifi. 2014. “Using Multi-sensor Data Fusion for Vibration Fault Diagnosis of Rolling Element Bearings by Accelerometer and Load Cell.” Information Fusion 18: 1–8.10.1016/j.inffus.2013.10.002
   Web of Science ®Google Scholar
• Shen, X., F. Tsung, and C. Zou. 2014. “A New Multivariate EWMA Scheme for Monitoring Covariance Matrices.” International Journal of Production Research 52 (10): 2834–2850.10.1080/00207543.2013.842019
   Web of Science ®Google Scholar
• Shi, J. 2013. “Data Fusion for in-Process Quality Improvement.” Proceedings of the 5th Manufacturing Engineering Society International Conference, Zaragoza, June.
   Google Scholar
• Suranthiran, S., and S. Jayasuriya. 2003. “Nonlinear Averaging of Multi-sensor Data.” Proceeding of International 19th Biennial Conference on Mechanical Vibration and NOISE, Parts A, B, and C, Chicago, September 2–6.
   Google Scholar
• Thomopoulos, S. C. 1989. “Sensor Integration and Data Fusion.” Proceedings of SPIE 1198, senor fusion II: Human and machine strategies, Philadelphia, 178–191.
   Google Scholar
• Tracy, N. D., J. C. Young, and R. L. Mason. 1992. “Multivariate Control Charts for Individual Observations.” Journal of Quality Technology 24 (2): 88–95.
   Web of Science ®Google Scholar
• Wang, F. 2006. “Quality Evaluation of a Manufactured Product with Multiple Characteristics.” Quality and Reliability Engineering International 22: 225–236.10.1002/(ISSN)1099-1638
   Web of Science ®Google Scholar
• Wang, H., L. Huwang, and J. H. Yu. 2015. “Multivariate Control Charts Based on the James-Stein Estimator.” European Journal of Operational Research 246: 119–127.10.1016/j.ejor.2015.02.046
   Web of Science ®Google Scholar
• Wierda, S. J. 1994. “Multivariate Statistical Process Control – Recent Results and Directions for Future Research.” Statistica Neerlandica 48 (2): 147–168.10.1111/j.1467-9574.1994.tb01439.x
   Google Scholar
• Yeh, A. B., L. Huwang, and C. Wu. 2005. “A Multivariate EWMA Control Chart for Monitoring Process Variability with Individual Observations.” IIE Transactions 37 (11): 1023–1035.10.1080/07408170500232263
   Web of Science ®Google Scholar
• Yeh, A. B., D. K. J. Lin, and R. N. McGrath. 2006. “Multivariate Control Charts for Monitoring Covariance Matrix: A Review.” Quality Technology and Quantitative Management 3 (4): 415–436.10.1080/16843703.2006.11673124
   Google Scholar
• Yeh, A. B., B. Li, and K. Wang. 2012. “Monitoring Multivariate Process Variability with Individual Observations via Penalised Likelihood Estimation.” International Journal of Production Research 50 (22): 6624–6638.10.1080/00207543.2012.676684
   Web of Science ®Google Scholar
• Yeong, W. C., M. B. C. Khoo, W. L. Teoh, and P. Castagliola. 2016. “A Control Chart for the Multivariate Coefficient of Variation.” Quality and Reliability Engineering International 32 (3): 1213–1225.10.1002/qre.v32.3
   Web of Science ®Google Scholar
• Zhang, G., and S. I. Chang. 2008. “Multivariate EWMA Control Charts Using Individual Observations for Process Mean and Variance Monitoring and Diagnosis.” International Journal of Production Research 46 (24): 6855–6881.10.1080/00207540701197028
   Web of Science ®Google Scholar