Fault diagnosis of industrial process based on the optimal parametric t-distributed stochastic neighbor embedding

Author:

Jia Ruixue,Wang Jing,Zhou Jinglin

Publisher

Springer Science and Business Media LLC

Subject

General Computer Science

Reference8 articles.

1. Zhang A, Peng K X, Shardt Y A W. A comparison of different statistics for detecting multiplicative faults in multivariate statistics-based fault detection approaches. IEEE Access, 2018, 96: 541–553

2. Zhu J L, Ge Z Q, Song Z H, et al. Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data. Annu Rev Control, 2018, 46: 107–133

3. He Q P, Qin S J, Wang J. A new fault diagnosis method using fault directions in Fisher discriminant analysis. AIChE J, 2005, 51: 555–571

4. Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290: 2323–2326

5. Maaten L V D. Learning a parametric embedding by preserving local structure. J Mach Learn Res, 2009, 5: 384–391

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