He Ming, Feng Bo-Qin, Ma Zhao-Feng, Fu Xiang-Hua. A unsupervised rough clustering method based on Gaussian mixture model. Journal of Harbin Institute of Technology, 2006, 38(2): 256-259, 322(何明, 冯博琴, 马兆丰, 傅向华. 一种基于高斯混合模型的无监督粗糙聚类方法. 哈尔滨工业大学学报, 2006, 38(2): 256-259, 322)
[2]
Figueiredo M A T, Jain A K. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(3): 381-396
[3]
Choi S W, Park J H, Lee I B. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis. Computers and Chemical Engineering, 2004, 28(8): 1377-1387
[4]
Chiang L H, Russell E, Braatz R D. Fault Detection and Diagnosis in Industrial Systems. New York: Springer Verlag, 2001. 36-37
[5]
Xie X, Shi H B. Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models. Industrial and Engineering Chemistry Research, 2012, 51(15): 5497-5505
[6]
Barry M W, Neal B G, Stephanie W B, Daniel D W J, Gabriel G B. A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process. Journal of Chemometrics, 1999, 13(3-4): 379-396
[7]
Cinar A, Parulekar S, Undey C, Birol G. Batch Fermentation: Modeling, Monitoring, and Control. New York: CRC Press, 2003.
[8]
Lee J M, Yoo C K, Lee I B. Enhanced process monitoring of fed-batch penicillin cultivation using time-varying and multivariate statistical analysis. Journal of Biotechnology, 2004, 110(2): 119-136
[9]
Nomikos P, MacGregor J F. Monitoring batch processes using multi-way principal component analysis. AIChE Journal, 1994, 40(8): 1361-1375
[10]
Kourti T, Nomikos P, MacGregor J F. Analysis, monitoring and fault diagnosis of batch processes using multi-block and multiway PLS. Journal of Process Control, 1995, 5(4): 277-284
[11]
Zhang Jia, Sun Wei, Zhao Jin-Song, Sun Mei-Hong. Fault detection of batch process based on multi-phase multiway principal component analysis. Computers and Applied Chemistry, 2010, 27(3): 298-302(张佳, 孙巍, 赵劲松, 孙美红. 多段MPCA法监测间歇过程的故障. 计算机与应用化学, 2010, 27(3): 298-302)
[12]
Chang Yu-Qing, Wang Zhu, Tan Shuai, Wang Fu-Li, Yang Jie. Research on multistage-based MPCA modeling and monitoring method for batch processes. Acta Automatica Sinica, 2010, 36(9): 1312-1320(常玉清, 王姝, 谭帅, 王福利, 杨洁. 基于多时段MPCA模型的间歇过程监测方法研究. 自动化学报, 2010, 36(9): 1312-1320)
[13]
Yu J, Qin S J. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models. AIChE Journal, 2008, 54(7): 1811-1829
[14]
Yu J, Qin S J. Multiway Gaussian mixture model based multiphase batch process monitoring. Industrial and Engineering Chemistry Research, 2009, 48(18): 8585-8594
[15]
Zhao Chun-Hui, Wang Fu-Li, Yao Yuan, Gao Fu-Rong. Phase-based statistical modeling, online monitoring and quality prediction for batch processes. Acta Automatica Sinica, 2010, 36(3): 366-374(赵春晖, 王福利, 姚远, 高福荣. 基于时段的间歇过程统计建模、在线监测及质量预报. 自动化学报, 2010, 36(3): 366-374)
[16]
Rothwell S G, Martin E B, Morris A J. Comparison of methods for dealing with uneven length batches. In: Proceedings of the 1998 International Conference on Computer Application in Biotechnology. Osaka, Japan: Elsevier, 1998. 387-392
[17]
Zhou Dong-Hua, Li Gang, Li Yuan. Fault Diagnosis Technology of Data-driven in Industrial Process. Beijing: Science Press, 2011. 39-41 (周东华, 李钢, 李元. 数据驱动的工业过程故障诊断技术. 北京: 科学出版社, 2011. 39-41)
