Erginel, N. (2014). Fuzzy rule-based $tilde p $ and $ ntilde p $ control charts. Journal of intelligent & fuzzy systems, 27(1), 159-171.
 Taleb, H., & Limam, M. (2002). On fuzzy and probabilistic control charts. International journal of production research, 40(12), 2849-2863.
 El-Shal, S. M., & Morris, A. S. (2000). A fuzzy rule-based algorithm to improve the performance of statistical process control in quality systems. Journal of intelligent & fuzzy systems, 9(3, 4), 207-223.
 Paul, A. K., Shill, P. C., Rabin, M. R. I., & Murase, K. (2018). Adaptive weighted fuzzy rule-based system for the risk level assessment of heart disease. Applied intelligence, 48(7), 1739-1756.
 Hsu, H. M., & Chen, Y. K. (2001). A fuzzy reasoning based diagnosis system for X control charts. Journal of intelligent manufacturing, 12(1), 57-64.
  Yang, X., Wang, Z., & Zi, X. (2017). Goodness-of-fit-based outlier detection for Phase I monitoring. Communications in Statistics - Simulation and Computation, 1–13.
 Sousa, S., Rodrigues, N., & Nunes, E. (2017). Application of SPC and quality tools for process improvement. Procedia manufacturing, 11, 1215-1222.
 Kaya, I., Erdoğan, M., & Yıldız, C. (2017). Analysis and control of variability by using fuzzy individual control charts. Applied soft computing, 51, 370-381.
 Fadaei, S., & Pooya, A. (2018). Fuzzy U control chart based on fuzzy rules and evaluating its performance using fuzzy OC curve. The TQM journal, 30(3), 232-247.
 Zhang, B., Yang, C., Zhu, H., Shi, P., & Gui, W. (2018). Controllable-domain-based fuzzy rule extraction for copper removal process control. IEEE transactions on fuzzy systems, 26(3), 1744-1756.
 Naik, N., Diao, R., & Shen, Q. (2018). Dynamic fuzzy rule interpolation and its application to intrusion detection. IEEE transactions on fuzzy systems, 26(4), 1878-1892.
 Keivanpour, S., Ait-Kadi, D., & Mascle, C. (2017). Automobile manufacturers’ strategic choice in applying green practices: joint application of evolutionary game theory and fuzzy rule-based approach. International journal of production research, 55(5), 1312-1335.
 Cheng, C. B. (2005). Fuzzy process control: Construction of control charts with fuzzy numbers. Fuzzy sets and systems, 154(2), 287-303.
 Faraz, A., & Moghadam, M. B. (2007). Fuzzy control chart a better alternative for Shewhart average chart. Quality & quantity, 41(3), 375-385.
 Erginel, N. (2008). Fuzzy individual and moving range control charts with α-cuts. Journal of intelligent & fuzzy systems, 19(4, 5), 373-383.
 Colubi, A. (2009). Statistical inference about the means of fuzzy random variables: Applications to the analysis of fuzzy-and real-valued data. Fuzzy sets and systems, 160(3), 344-356.
 Kanagawa, A., Tamaki, F., & Ohta, H. (1993). Control charts for process average and variability based on linguistic data. The international journal of production research, 31(4), 913-922.
 Kaya, İ., & Kahraman, C. (2011). Process capability analyses based on fuzzy measurements and fuzzy control charts. Expert systems with applications, 38(4), 3172-3184.
 Faraz, A., & Shapiro, A. F. (2010). An application of fuzzy random variables to control charts. Fuzzy sets and systems, 161(20), 2684-2694.
 Laviolette, M., Seaman, J. W., Barrett, J. D., & Woodall, W. H. (1995). A probabilistic and statistical view of fuzzy methods. Technometrics, 37(3), 249-261.
 Marcucci, M. (1985). Monitoring multinomial processes. Journal of quality technology, 17(2), 86-91.
 Saaty, T. L. (1974). Measuring the fuzziness of sets. Taylor & Francis, 4(4), 53-61.
 Wang, J. H., & RAZ, T. (1990). On the construction of control charts using linguistic variables. The international journal of production research, 28(3), 477-487.
 Woodall, W. H. (1997). Control charts based on attribute data: Bibliography and review. Journal of quality technology, 29(2), 172-183.
 Woodall, W. H., Tsui, K. L., & Tucker, G. R. (1997). A review of statistical and fuzzy quality control charts based on categorical data. Frontiers in statistical quality control (pp. 83-89). Physica, Heidelberg.
 Montgomery, D. C. (2009). Introduction to statistical quality control. John Wiley & Sons (New York).