[1]
|
Garcia-Alvarez D, Fuente M J, Sainz G I. Fault detection and isolation in transient states using principal component analysis. Journal of Process Control, 2012, 22(3): 551-563
|
[2]
|
Shi Huai-Tao, Liu Jian-Chang, Ding Xiao-Di, Tan Shuai, Wang Xue-Mei. Fault detection based on hybrid dynamic principal component analysis. Control Engineering of China, 2012, 19(1): 148-151 (in Chinese)
|
[3]
|
Wen Cheng-Lin, Hu Yu-Cheng. Fault diagnosis based on information incremental matrix. Acta Automatica Sinica, 2012, 38(5): 832-840 (in Chinese)
|
[4]
|
Wu Feng, Zhong Yan, Wu Quan-Yuan. Online classification framework for data stream based on incremental kernel principal component analysis. Acta Automatica Sinica, 2010, 36(4): 534-542 (in Chinese)
|
[5]
|
Dobos L, Abonyi J. On-line detection of homogeneous operation ranges by dynamic principal component analysis based time-series segmentation. Chemical Engineering Science, 2012, 75: 96-105
|
[6]
|
Zhang Y W, Zhou H, Qin S J. Decentralized fault diagnosis of large-scale processes using multiblock kernel principal component analysis. Acta Automatica Sinica, 2010, 36(4): 593-597
|
[7]
|
Luan Fang-Jun, Guo Hong-Mei, Lin Lan, Wang Yong-Hui. Algorithm of face authentication based on wavelet transform and combining two different 2-dimensional participial component analyses. Journal of Shenyang Jianzhu University (Natural Science), 2010, 26(5): 1001-1005 (in Chinese)
|
[8]
|
Chiang L H, Russell E L, Braatz R D. Fault Detection and Diagnosis in Industrial Systems. Berlin: Springer, 2001
|
[9]
|
Wen Cheng-Lin, Hu Jing, Wang Tian-Zhen, Chen Zhi-Guo. Relative PCA with applications of data compression and fault diagnosis. Acta Automatica Sinica, 2008, 34(9): 1128-1139 (in Chinese)
|
[10]
|
Dunia R, Qin S J, Edgar T F, McAvoy T J. Identification of faulty sensors using principal component analysis. American Institute of Chemical Engineers Journal, 1996, 42(10): 2797-2812
|
[11]
|
Zhao Zhong-Gai, Liu Fei. Application research of statistical monitoring index based on Mahalanobis distance. Acta Automatica Sinica, 2008, 34(4): 493-495 (in Chinese)
|
[12]
|
Wen G H, Jiang L J, Wen J. Local relative transformation with application to isometric embedding. Pattern Recognition Letters, 2009, 30(3): 203-211
|
[13]
|
Li Yuan, Tang Xiao-Chu. Improved performance of fault detection based on selection of the optimal number of principal components. Acta Automatica Sinica, 2009, 35(2): 1550-1557 (in Chinese)
|
[14]
|
Tamura M, Tsujita S. A study on the number of principal components and sensitivity of fault detection using PCA. Computers and Chemical Engineering, 2007, 31(9): 1035-1046
|
[15]
|
Banerjee A, Burlina P. Efficient particle filtering via sparse kernel density estimation. IEEE Transactions on Image Processing, 2010, 19(9): 2480-2490
|
[16]
|
Shi H T, Liu J C, Zhang Y, Li L. Fault detection method based on relative-transformation partial least squares. Chinese Journal of Scientific Instrument, 2012, 33(4): 816-822
|
[17]
|
Marcu T, Köppen-Seliger B, Stücher R. Design of fault detection for a hydraulic looper using dynamic neural networks. Control Engineering Practice, 2008, 16(2): 192-213
|
[18]
|
Yue Yu-Mei, Ma Ya-Li, Wang Jiao-Qing. Research about fault intelligent diagnosis of high speed rotating machinery. Journal of Shenyang Jianzhu University (Natural Science), 2005, 21(6): 770-773 (in Chinese)
|