Name
Title | ||
|---|---|---|
An Improved Mixture of Probabilistic PCA for Nonlinear Data-Driven Process Monitoring. |
Abstract | ||
|---|---|---|
An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analyzers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is propo... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCYB.2017.2771229 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Principal component analysis,Monitoring,Probabilistic logic,Analytical models,Data models,Computational modeling,Fault diagnosis | Data mining,Data modeling,Nonlinear system,Data-driven,Statistic,Fault detection and isolation,Artificial intelligence,Probabilistic principal component analysis,Probabilistic logic,Principal component analysis,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
49 | 1 | 2168-2267 |
Citations | PageRank | References |
7 | 0.45 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingxin Zhang | 1 | 264 | 68.81 |
| Hao Chen | 2 | 13 | 4.00 |
| Songhang Chen | 3 | 7 | 0.45 |
| X. Hong | 4 | 157 | 11.12 |