Lee Jong-Min, Yoo Chang Kyoo, Choi Sang Wook, Vanrolleghem Peter A, Lee In-Beum. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science, 2004, 59: 223-234
[2]
Yao Yuan, Gao Furong. Statistical monitoring and fault diagnosis of batch process using two-dimensional dynamic information[J]. Industrial Engineering Chemical Research, 2010, 49: 9961-9969
[3]
Ge Zhiqiang, Song Zhihuan, Gao Furong. Review of recent research on data-based process monitoring[J]. Industrial Engineering Chemical Research, 2013, 52: 3543-3562
[4]
Zhao Shijian, Zhang Jie, Xu Yongmao. Monitoring of processes with multiple operation modes through multiple principal component analysis models[J]. Industrial Engineering Chemical Research, 2004, 43: 7025-7035
[5]
Ge Zhiqiang, Song Zhihuan. Mixture Bayesian regularization method of PPCA for multimode process monitoring[J]. American Institute of Chemical Engineers, 2010, 56(11): 2838-2849
[6]
Xie Xiang, Shi Hongbo. Dynamic multimode process modeling and monitoring using adaptive Gaussian mixture models[J]. Industrial Engineering Chemical Research, 2012, 51: 5497-5505
[7]
Yu Jie. A nonlinear kernel Gaussian mixture model based inferential monitoring approach for fault detection and diagnosis of chemical processes[J]. Chemical Engineering Science, 2011, 68: 506-519
[8]
Zhao Chunhui, Gao Furong, Wang Fuli. Nonlinear batch process monitoring using phase-based kernel-independent component analysis-principal component analysis (KICA-PCA)[J]. Industrial Engineering Chemical Research, 2009, 48: 9163-9174
[9]
Zhou Donghua, Hu Yanyan. Fault diagnosis techniques for dynamic systems[J]. Acta Automatica Sinica, 2009, 35(6): 748-758
[10]
Kruger Uwe, Dimitriadis Grigorios. Diagnosis of process faults in chemical systems using a local partial least squares approach[J]. American Institute of Chemical Engineers, 2008, 54: 2581-2596
[11]
Ge Zhiqiang, Song Zhihuan. Maximum-likelihood mixture factor analysis model and its application for process monitoring[J]. Chemometrics and Intelligent Laboratory Systems, 2010, 102: 53-61
[12]
Ge Zhiqiang, Song Zhihuan. Process monitoring based on independent component analysis-principal component analysis (ICA-PCA) and similarity factors[J]. Industrial Engineering Chemical Research, 2007, 46: 2054-2063
[13]
Zhang Yingwei. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM[J]. Chemical Engineering Science, 2009, 64(5): 801-811
[14]
Ma Yuxin(马玉鑫), Wang Mengling(王梦灵), Shi Hongbo(侍洪波). Fault detection for chemical process based on locally linear embedding[J]. CIESC Journal (化工学报), 2012, 63(7): 2121-2127
[15]
Hu Kunlun, Yuan Jingqi. Statistical monitoring of fed-batch process using dynamic multiway neighborhood preserving embedding[J]. Chemometrics and Intelligent Laboratory Systems, 2008, 90: 195-203
[16]
Lee Jaeshin, Kang Bokyoung, Kang Suk-Ho. Integrating independent component analysis and local outlier factor for plant-wide process monitoring[J]. Journal of Process Control, 2011, 21: 1011-1021
[17]
Ma Hehe, Hu Yi, Shi Hongbo. Fault detection and identification based on neighborhood standardized local factor method[J]. Industrial Engineering Chemical Research, 2012, 52: 2389-2402
[18]
Duan Lian, Xiong Deyi, Lee Jun, Guo Feng. A local-density based spatial clustering algorithm with noise//IEEE International Conference on System, Man, and Cybernetics[C].Taipei, 2006: 4061-4066
[19]
Ge Zhiqiang, Song Zhihuan. Multimode process monitoring based on Bayesian method[J]. Chemometrics and Intelligent Laboratory Systems, 2009, 23: 636-650
[20]
Downs J J, Vogel E F. A plant-wide industrial process control problem[J]. Computers and Chemical Engineering, 1993, 17(3): 245-255
[21]
Ricker N L. Optimal steady-state operation of the Tennessee Eastman challenge process[J]. Computers and Chemical Engineering, 1995, 19(9): 949-959
