Intelligent supervision approach based on multilayer neural PCA and nonlinear gain scheduling
| Autor: | Lotfi Nabli, Hanen Chaouch, S. Charfedine, Houssem Jerbi, Khaled Ouni |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science 02 engineering and technology Fault detection and isolation Scheduling (computing) 020901 industrial engineering & automation Gain scheduling Artificial Intelligence Control theory Linearization Principal component analysis 0202 electrical engineering electronic engineering information engineering Process control 020201 artificial intelligence & image processing Feedback linearization Software |
| Zdroj: | Neural Computing and Applications. 31:1153-1163 |
| ISSN: | 1433-3058 0941-0643 |
| DOI: | 10.1007/s00521-017-3147-9 |
| Popis: | This paper is mainly aimed at developing an off-line supervision approach geared to complex processes. This approach consists of two parts: the first part is the fault detection and isolation and the second one is the process control. The first part is devoted to the implementation of the multilayer neural PCA which combines the advantage of data reduction provided by the principal component analysis and the power of neural network linearization. The transition to control is conditioned by the absence of faults in the process; if there is a defect, it must be isolated by identifying the defected variables. The second part rests on the combination of two control tools: both the gain scheduling and the feedback linearization yield a new approach called nonlinear gain scheduling. To have our work validated, we applied it to a photovoltaic system and it gave effective results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink