Alamelu Manghai, T., & Jegadeeshwaran, R. (2019). Vibration based brake health monitoring using wavelet features: A machine learning approach. Journal of Vibration and Control, 25(18),2534–2550.

Al-Assaf, Y. (2004). Recognition of control chart patterns using multi-resolution wavelets analysis and neural networks. Computer & Industrial Engineering, 47 (17-29), 17-29.

Atashgar, K., & Abbasi, L. (2021). A new model to monitor very small effects of a polynomial profile. International Journal of Quality & Reliability Management, 38(4), 1023-143.

Atashgar, K., & Zargarabadi, O. A. (2017). Monitoring multivariate profile data in plastic parts manufacturing industries: An intelligently data processing. Journal of Industrial Information Integration, 8, 38-48.

Atashgar, K., & Noorossana, R. (2012). Diagnosing the source(s) of a monotonic change in the process mean vector. International Journal of Advanced Manufacturing Technology, 60, 1175–1183.

Cahng, S.l., & Yadama, S. (2010). Statistical process control for monitoring non-linear profiles using wavelet filtering and B-Spline approximation. International Journal of Production Research, 48 (4), 1049-1068.

Chen, Z., Lu, S. & Lam, S. (2007). A hybrid system for SPC concurrent pattern recognition. Advanced Engineering Informatics 21(3,: 303–310.

Chicken, E., Pignatiello Jr, J. J., & Simpson, J. R. (2009). Statistical process monitoring of nonlinear profiles using wavelets. Journal of Quality Technology, 41(2), 198-212.

Cohen, A., & Atoui, M.A.A. (2022). On Wavelet-based Statistical Process Monitoring. Transactions of the Institute of Measurement and Control, 44 (3), 525-538.

Cohen, A., Tiplica, T., & Kobi, A. (2015). Statistical process control for AR(1) or non-gaussian processes using wavelets coefficients. Journal of Physics: Conference Series. 659 012043.

Cohen, A., Tiplica, T., & Kobi, A. (2016a). Owave control chart for monitoring the process mean. Control Engineering Practice, 54, 223–230.

Cohen, A., Tiplica, T., & Kobi, A. (2016b). Design of experiments and statistical process control using wavelets analysis. Control Engineering Practice, 49, 129–138

Crosier, R.B. (1988). Multivariate Generalization of Cumulative Sum Quality Control Schemes. Technometrics. 30(3), 291-302.

Du, S., Huang, D., & Lv, J. (2013). Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines. Computers and Industrial Engineering, 66(4), 683–695.

Fasso, A. (1999). One-sided MEWMA control charts. Communications in Statistics -Theory and Methods. 28(2), 381-401.

Fleet, P.J.V. (2019). Discrete Wavelet Transformations: An Elementary Approach with Applications, New York, John Wiley & Sons, Inc.

Gao, R.X., & Yan, R. (2010). Wavelets: Theory and Applications for Manufacturing. New York: Springer

Gharib, A., Amiri, A., & Jalilibal, Z. (2021). Designing a multivariate exponentially weighted moving average control chart with measurement errors. Journal of Quality Engineering and Production Optimization, 5(1), 215-232.

Guh, R.S. (2007). On-line Identification and Quantification of Mean Shifts in Bivariate Processes using a Neural Network-based Approach. Quality and Reliability Engineering International. 23(3), 367-385.

Guh, R.S., & Shiue, Y.R. (2008). An effective application of decision tree learning for on-line detection of mean shift in multivariate control charts. Computer and Industrial Engineering. 55(2), 475-493.

Haq, A., Munir, T.,  &  Shah, B.A. (2020). Dual multivariate CUSUM charts with auxiliary information for process mean. Quality and Reliability Engineering International. 36(3), 861-875.

Harrou, F., Taghezouit, B., & Sun, Y. (2018a). A robust monitoring technique for fault detection in grid-connected PV plants. In: 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), Istanbul, Turkey, 594–598.

Harrou, F., Khaldi, B., Sun, Y., & Cherif, F. (2018b). Statistical detection of faults in swarm robots under noisy conditions. In: 2018 6th International Conference on Control Engineering & Information Technology (CEIT), Istanbul, Turkey, 1–6. IEEE.

Harrou, F., Taghezouit, B., & Sun, Y. (2019). Robust and flexible strat-egy for fault detection in grid-connected photovoltaic systems. Energy Conversion and Management. 180, 1153–1166.

Hotelling, H. (1947) Multivariate quality control-Illustrated by the air testing of sample bombsights. Techniques of Statistical Analysis, Eisenhart, C., Hastay, M.W., Wallis, W.A. (Eds). McGraw-Hill: New York.

Huwang, L., Lin, L.W., & Yu, C.T. (2019). A spatial rank–based multivariate EWMA chart for monitoring process shape matrices. Quality and Reliability Engineering International, 35(6), 1716-1734.

Hwarng, H.B. (2004). Detecting process mean shift in the presence of autocorrelation: a neural network based monitoring scheme. International Journal of Production Research, 42(3),573-595.

Jeong, M.K., Lu, J.C., Huo, X., Vidakovic, B., & Chen, D. (2006). Wavelet-based data reduction techniques for process fault detection. Technometrics, 48(1): 26–40.

Jeong, Y.S., Kim, B. & Ko, Y.D. (2013). Exponentially weighted moving average-based procedure with adaptive thresholding for monitoring nonlinear profiles: Monitoring of plasma etch process in semiconductor manufacturing. Expert Systems with Applications, 40(14), 5688–5693.

Koosh, M., Noorossana, R., & Ahmadi, O. (2022). Two-dimensional wavelet based statistical monitoring of image data. Quality & Reliability Engineering International. 38(7), 3797-3815.

Lee, J., Hur, Y., Kim, S.H, & Wilson, J.R. (2012). Monitoring nonlinear profiles using a wavelet-based distribution-free CUSUM chart. Journal of Production Research, 50(22), 6574–6594.

Li, J., Jeske, D.R., Zhou, Y., & Zhang, X. (2019). A wavelet-based non-parametric CUSUM control chart for autocorrelated processes with applications to network surveillance. Quality and Reliability Engineering International, 35(2), 644–658.

Lowry, C.A., Woodall, W.H., Champ, C.W., & Rigdon, S.E. (1992). A multivariate Exponential Weighted Moving Average control chart. Technometrics. 34(1), 46-53.

Mansouri, M., Al-Khazraji, A., Hajji, M., Faouzi Harkat, M., Nounou, H., & Numan, M.  (2018). Wavelet optimized EWMA for fault detection and application to photovoltaic systems. Solar Energy, 167, 125–136.

McGinnity, K., Chicken, E., & Pignatiello, J.J. (2015). Nonparametric changepoint estimation for sequential nonlinear profile monitoring. Quality and Reliability Engineering International, 31(1), 57–73.

Ngai, H.M., & Zhang, J. (2001). Multivariate cumulative sum control charts based on projection pursuit. Statistica Sinica. 11(3), 747-766.

Nikoo, M., & Noorossana, R. (2013). Phase ii monitoring of nonlinear profile variance using wavelet. Quality and Reliability Engineering International, 29(7), 1081–1089.

Noorossana, R., Atashgar, K., & Saghaie, A. (2011). An integrated supervised learning solution for monitoring process mean vector. International Journal of Advanced Manufacturing Technology, 56, 755–765

Pignatiello, J.J., & Runger, G.C. (1990). Comparisons of multivariate CUSUM charts. Journal of Quality Technology, 22(3), 173-186.

Piri, S., Abdel-salam, G., & Boone, E.L. (2021). A wavelet approach for profile monitoring of Poisson distribution with application. Communication in Statistics- Simulation and Computation, 50(2), 525-536.

Ranaee, V., & Ebrahimzadeh, A. (2011). Control chart pattern recognition using a novel hybrid intelligent method. Applied Soft Computing 11(2), 2676–2686.

Runger, G.C., Keats, J.B., Montgomery, D.C., & Scranton, R.D. (1999). Improving the performance of a multivariate exponentially weighted moving average control chart. Quality and Reliability Engineering International. 15(3), 161-166.

Runger, G.C., & Testik, M.C. (2004). Multivariate extensions to cumulative sum control charts. Quality and Reliability Engineering International, 20(6), 587-606.

Testik, M.C., & Borror, C.M. (2004). Design strategies for the multivariate exponentially weighted moving average control chart. Quality and Reliability Engineering International, 20(6), 571-   577.

Stewarda, R.M., & Rigdon, S.E. (2015). The Efficiency of a Wavelet Transformation Approach to Prospective Monitoring, Quality and Reliability Engineering International, 31(3), 489-500.

Tseng, S., Chou, R., & Lee, S. (2002). A study on a multivariate EWMA controller. IIE Transactions, 34(6), 541-549.

Wang, C.H., & Kuo, W. (2007). Identification of control chart patterns using wavelet filtering and robust fuzzy clustering. Journal of Intelligent Manufacturing, 18(3), 343–350.

Wang, H., Kim, S.H., Huo, X., Hur, Y., & Wilson, J. (2015). Monitoring nonlinear profiles adaptively with a wavelet-based distribution-free CUSUM chart. International Journal of Production Research, 53(15), 4648–4667.

Woodall, W.H., & Ncube, M.M. (1985) Multivariate CUSUM quality- control procedures.  Technimetrics, 27(3), 285-292

Xie, F., Sun, J.,  Castagliola, P., Hu, X., & Tang, A. (2021) . A multivariate CUSUM control chart for monitoring Gumbel's bivariate exponential data. Quality & Reliability Engineering International, 37(1), 10-33.

Yeh, A.B., Lin, D.K.J., Zhou, H., & Venkataramani, C. (2003). A multivariate exponentially weighted moving average control chart for monitoring process variability. Journal of Applied Statistics, 30(5), 507-536.

Yin, Z., & Hou, J. (2016). Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing, 174, 643–650.

Yumin, L. (1996). An improvement for MEWMA in multivariate process control. Computers and Industrial Engineering, 31(3-4), 779-781.