References
- Cheng, C. S., Cheng, H. P. (2008). Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines. Expert Systems With Applications 35:198–206.
- Hou, C. D., Shao, Y. E., Huang, S. (2013). A combined MLE and generalized P chart approach to estimate the change point of a multinomial process. Applied Mathematics & Information Sciences 7(4):1487–1493.
- Levinson, W. (1997). Watch out for nonnormal distributions of impurities. Chemical Engineering Progress 93(5):70–76.
- Levinson, W., Polny, A. (1999). SPC for tool particle counts. Semiconductor International June:117–122.
- Montgomery, D. C. (2009). Introduction to Statistical Quality Control. 6th ed. New York: Wiley.
- Pignatiello, J. J., Samuel, T. R. (2001). Estimation of the change point of a normal process mean in SPC applications. Journal of Quality Technology 33(1):82–95.
- Samuel, T. R., Pignatiello, J. J., Calvin, J. A. (1998). Identifying the time of a step change with control charts. Quality Engineering 10(3):521–527.
- Shao, Y. E., Hou, C. D. (2004). A study on identifying the time of a step change disturbance with S control charts and MLE method. Journal of the Chinese Institute of Industrial Engineers 21(4):349–357.
- Shao, Y. E., Hou, C. D. (2006). Estimation of the starting time of a step change disturbance in a gamma process. Journal of the Chinese Institute of Industrial Engineers 23(4):319–327.
- Shao, Y. E., Hou, C. D., Wang, H. J. (2006). Estimation of the change point of a gamma process by using the S control chart and MLE. Journal of the Chinese Institute of Industrial Engineers 23(3):207–214.
- Shao, Y. E., Hou, C. D. (2013a). Fault identification in industrial processes using an integrated approach of neural network and analysis of variance. Mathematical Problems in Engineering 2013:1–7.
- Shao, Y. E., Hou, C. D. (2013b). Change point determination for a multivariate process using a two-stage hybrid scheme. Applied Soft Computing 13(3):1520–1527.
- Shao, Y. E., Hsu, B. S. (2009). Determining the contributors for a multivariate SPC chart signal using artificial neural networks and support vector machine. International Journal of Innovative Computing, Information and Control 5(12B):4899–4906.