Process Modeling and Monitoring With Incomplete Data Based on Robust Probabilistic Partial Least Square Method
In real industrial processes, both outliers and missing data are very common. Owing to the assumption that the data sampled from a normal process follow the Gaussian distribution, the regular datadriven process monitoring methods, such as the probabilistic partial least square (PPLS) method and the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8304745/ |